College of Habour, Coastal and Offshore Engineering, Hohai University, No.1, Xikang Road, Nanjing 210098, China.
College of Civil and Transportation Engineering, Hohai University, No.1, Xikang Road, Nanjing 210098, China.
Int J Environ Res Public Health. 2020 Aug 9;17(16):5754. doi: 10.3390/ijerph17165754.
To analyze the time-frequency characteristics of the particulate matter (PM) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM concentration level so as to support the formulation of air quality improvement plans around port areas. In this study, the Hilbert-Huang transform (HHT) method was used to analyze the time-frequency characteristics of the PM concentration data series measured at three different sites at the Xinglong Port of Zhenjiang, China, over three months. The HHT method consists of two main stages, namely, empirical mode decomposition (EMD) and Hilbert spectrum analysis (HSA), where the EMD technique is used to pre-process the HSA in order to determine the intrinsic mode function (IMF) components of the raw data series. The results show that the periods of the IMF components exhibit significant differences, and the short-period IMF component provides a modest contribution to all IMF components. Using HSA technology for these IMF components, we discovered that the variations in the amplitude of the PM concentration over time and frequency are discrete, and the range of this variation is mainly concentrated in the low-frequency band. We inferred that long-term influencing factors determine the PM concentration level in the port, and short-term influencing factors determine the difference in concentration data at different sites. Therefore, when formulating PM emission mitigation strategies, targeted measures must be implemented according to the period of the different influencing factors. The results of this study can help guide recommendations for port authorities when formulating the optimal layout of measurement devices.
为了分析颗粒物(PM)浓度的时频特征,使用干散货港口测量的数据集来确定不同时期各种因素对 PM 浓度水平的贡献,从而支持制定港口区域周边的空气质量改善计划。在这项研究中,使用希尔伯特-黄变换(HHT)方法分析了中国镇江兴隆港三个不同地点三个月来测量的 PM 浓度数据序列的时频特征。HHT 方法由两个主要阶段组成,即经验模态分解(EMD)和希尔伯特谱分析(HSA),其中 EMD 技术用于预处理 HSA,以确定原始数据序列的固有模态函数(IMF)分量。结果表明,IMF 分量的周期表现出显著差异,短周期 IMF 分量对所有 IMF 分量的贡献不大。对这些 IMF 分量使用 HSA 技术,我们发现 PM 浓度随时间和频率的幅度变化是离散的,这种变化的范围主要集中在低频带。我们推断,长期影响因素决定了港口的 PM 浓度水平,短期影响因素决定了不同地点的浓度数据差异。因此,在制定 PM 排放缓解策略时,必须根据不同影响因素的周期实施有针对性的措施。本研究的结果可以帮助指导港口当局在制定最佳测量设备布局时提出建议。