A hierarchical Bayesian model for the analysis of space-time air pollutant concentrations and an application to air pollution analysis in Northern China

Ding, Weifu; Leung, Yee; Zhang, Jiangshe; Fung, Tung

doi:10.1007/s00477-021-02027-8

Cited by 6 publications

(2 citation statements)

References 45 publications

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“…In environmental sciences, datasets are usually generated by tracking certain variables of interest at different spatial locations over time. Scientists measure soil features or pollutants at different monitoring points over a territory and throughout a certain span of time, as in De Iaco et al (2019); Ding et al (2021); Wang et al (2021) to name some examples. The analysis of such spatio-temporal data needs to account for possible dependence in space and time for each measured variable but also in-between variables.…”

Section: Introductionmentioning

confidence: 99%

Blind recovery of sources for multivariate space-time random fields

Muehlmann

Iaco

Nordhausen

2022

Stoch Environ Res Risk Assess

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With advances in modern worlds technology, huge datasets that show dependencies in space as well as in time occur frequently in practice. As an example, several monitoring stations at different geographical locations track hourly concentration measurements of a number of air pollutants for several years. Such a dataset contains thousands of multivariate observations, thus, proper statistical analysis needs to account for dependencies in space and time between and among the different monitored variables. To simplify the consequent multivariate spatio-temporal statistical analysis it might be of interest to detect linear transformations of the original observations that result in straightforward interpretative, spatio-temporally uncorrelated processes that are also highly likely to have a real physical meaning. Blind source separation (BSS) represents a statistical methodology which has the aim to recover so-called latent processes, that exactly meet the former requirements. BSS was already successfully used in sole temporal and sole spatial applications with great success, but, it was not yet introduced for the spatio-temporal case. In this contribution, a reasonable and innovative generalization of BSS for multivariate space-time random fields (stBSS), under second-order stationarity, is proposed, together with two space-time extensions of the well-known algorithms for multiple unknown signals extraction (stAMUSE) and the second-order blind identification (stSOBI) which solve the formulated problem. Furthermore, symmetry and separability properties of the model are elaborated and connections to the space-time linear model of coregionalization and to the classical principal component analysis are drawn. Finally, the usefulness of the new methods is shown in a thorough simulation study and on a real environmental application.

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Section: Introductionmentioning

confidence: 99%

Blind recovery of sources for multivariate space-time random fields

Muehlmann

Iaco

Nordhausen

2022

Stoch Environ Res Risk Assess

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“…One of the most important processes in monitoring and assessing air pollution behaviors is the analysis of recorded air pollution data over time. Most of the available literature investigates the behaviors of air pollution data using various statistical models, including the time series approach [9][10][11][12], regression technique [13,14], stochastic analysis [15,16], distribution models [17][18][19], neural network and deep learning [20][21][22], spatial-temporal [23][24][25], extreme-value analysis [26,27], and multivariate approach [28,29]. All of these methods provide valuable information about the behaviors, trends, and dependency structures of air pollution characteristics.…”

Section: Introductionmentioning

confidence: 99%

Multifractal Characteristics on Temporal Maximum of Air Pollution Series

Masseran

2022

Mathematics

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Presenting and describing a temporal series of air pollution data with longer time lengths provides more concise information and is, in fact, one of the simplest techniques of data reduction in a time series. However, this process can result in the loss of important information related to data features. Thus, the purpose of this study is to determine the type of data characteristics that might be lost when describing data with different time lengths corresponding to a process of data reduction. In parallel, this study proposes the application of a multifractal technique to investigate the properties on an air pollution series with different time lengths. A case study has been carried out using an air pollution index data in Klang, Malaysia. Results show that hourly air pollution series contain the most informative knowledge regarding the behaviors and characteristics of air pollution, particularly in terms of the strength of multifractality, long-term persistent correlations, and heterogeneity of variations. On the other hand, the statistical findings found that data reduction corresponding to a longer time length will change the multifractal properties of the original data.

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A hybrid Daily PM2.5 concentration prediction model based on secondary decomposition algorithm, mode recombination technique and deep learning

Sun

2022

Stoch Environ Res Risk Assess

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A hierarchical Bayesian model for the analysis of space-time air pollutant concentrations and an application to air pollution analysis in Northern China

Cited by 6 publications

References 45 publications

Blind recovery of sources for multivariate space-time random fields

Blind recovery of sources for multivariate space-time random fields

Multifractal Characteristics on Temporal Maximum of Air Pollution Series

A hybrid Daily PM2.5 concentration prediction model based on secondary decomposition algorithm, mode recombination technique and deep learning

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