Time–Frequency Analysis of Particulate Matter (PM₁₀) Concentration in Dry Bulk Ports Using the Hilbert–Huang Transform

• Main Authors: Xuejun Feng, Jinxing Shen, Haoming Yang, Kang Wang, Qiming Wang, Zhongguo Zhou
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: ambient particulate matter; Hilbert–Huang transform (HHT); empirical mode decomposition (EMD); intrinsic mode functions (IMF); Hilbert spectrum analysis (HSA)
• Online Access: https://www.mdpi.com/1660-4601/17/16/5754
• ISSN: 1661-7827; 1660-4601

To analyze the time–frequency characteristics of the particulate matter (PM₁₀) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM₁₀ 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 PM₁₀ 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 PM₁₀ 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 PM₁₀ concentration level in the port, and short-term influencing factors determine the difference in concentration data at different sites. Therefore, when formulating PM₁₀ 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.