A statistical test of phase difference via wavelet method and its application to the spread of air pollution

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-09-05 DOI:10.1007/s00477-024-02801-4

Zhen Wan, Keyao Wei, Yingcun Xia

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引用次数: 0

Abstract

Accurate measurement and inference of phase difference between two time series are critical across several fields, including signal processing, economic dynamics, and air pollution research. Wavelet methods offer advantages over traditional approaches by allowing time–frequency localization and adaptability to non-stationary signals, which makes them widely used for phase difference estimation. However, existing methods do not provide a statistical test to determine whether a measured phase difference reflects a true underlying relationship between the signals or is merely an artifact of measurement errors or randomness. In this paper, we propose a bootstrap method to fill this gap. Our method is particularly suited to the analysis of non-standard data distributions and complex temporal dependencies. Extensive simulations demonstrate its desirable power and control of type-I error. Furthermore, we apply the method to study air pollution dispersion in China and elucidate the factors influencing phase differences.

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小波法相位差统计检验及其在空气污染传播中的应用

精确测量和推断两个时间序列之间的相位差在信号处理、经济动力学和空气污染研究等多个领域都至关重要。与传统方法相比，小波方法具有时频定位和适应非稳态信号的优势，因此被广泛用于相位差估计。然而，现有的方法无法提供统计检验，以确定测得的相位差是反映了信号之间真正的潜在关系，还是仅仅是测量误差或随机性的伪影。在本文中，我们提出了一种自举法来填补这一空白。我们的方法特别适用于分析非标准数据分布和复杂的时间依赖关系。大量的模拟证明了该方法的理想能力和对 I 类误差的控制。此外，我们还将该方法用于研究中国的空气污染扩散情况，并阐明了影响相位差的因素。

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来源期刊

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

CiteScore

7.10

自引率

9.50%

发文量

189

审稿时长

3.8 months

期刊介绍： Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.