Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

Masseran, Nurulkamal; Safari, Muhammad Aslam Mohd

doi:10.3390/ijerph18136754

Cited by 7 publications

(4 citation statements)

References 59 publications

(79 reference statements)

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“…In general, a high API indicates poor air quality [51]. Details regarding the calculation of API data can be referred to in Masseran and Safari [52] and Masseran [53]. This study investigates the data of an air pollutant index (API) series in Klang for the period of 1 January 2002 to 31 August 2020.…”

Section: Study Area and Datamentioning

confidence: 99%

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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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Section: Study Area and Datamentioning

confidence: 99%

“…In general, a high API indicates poor air quality [51]. Details regarding the calculation of API data can be referred to in Masseran and Safari [52] and Masseran [53].…”

Section: Study Area and Datamentioning

confidence: 99%

Multifractal Characteristics on Temporal Maximum of Air Pollution Series

Masseran

2022

Mathematics

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“…Based on each individual indices, their values can then be integrated based on the highest sub-indices to determine the API indices at particular times [45]. A detailed calculation on the standardization and determination of the sub-indices values for each pollutant variable can be referred to in Masseran and Safari [46]. Figure 2 illustrate the schematic process for determining the API value based on five pollutant variables [47].…”

Section: Study Area and Datamentioning

confidence: 99%

Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

Masseran

2021

IJERPH

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This study proposes the concept of duration (D) and severity (S) measures, which were derived from unhealthy air pollution events. In parallel with that, the application of a copula model is proposed to evaluate unhealthy air pollution events with respect to their duration and severity characteristics. The bivariate criteria represented by duration and severity indicate their structural dependency, long-tail, and non-identically marginal distributions. A copula approach can provide a good statistical tool to deal with these issues and enable the extraction of valuable information from air pollution data. Based on the copula model, several statistical measurements are proposed for describing the characteristics of unhealthy air pollution events, including the Kendall’s τ correlation of the copula, the conditional probability of air pollution severity based on a given duration, the joint OR/AND return period, and the conditional D|S and conditional S|D return periods. A case study based on air pollution data indices was conducted in Klang, Malaysia. The results indicate that a copula approach is beneficial for deriving valuable information for planning and mitigating the risks of unhealthy air pollution events.

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“…In [21], the Hierarchical-Generalized Pareto model is applied to API data from different locations to provide a precise estimation of the return levels for each location. Moreover, the mixed peak-overthreshold-block-maxima (POT-BM) approach was used to investigate the unhealthy events and showed that this approach has an excellent tradeoff between bias and variance in modeling the extreme events [22].…”

Section: Introductionmentioning

confidence: 99%

Modeling Asymmetric Dependence Structure of Air Pollution Characteristics: A Vine Copula Approach

Ismail,

Masseran,

Alias

et al. 2024

Mathematics

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Contaminated air is unhealthy for people to breathe and live in. To maintain the sustainability of clean air, air pollution must be analyzed and controlled, especially after unhealthy events. To do so, the characteristics of unhealthy events, namely intensity, duration, and severity are studied using multivariate modeling. In this study, the vine copula approach is selected to study the characteristics data. Vine copula is chosen here because it is more potent than the standard multivariate distributions, and multivariate copulas, especially in modeling the tails related to extreme events. Here, all nine different vine copulas are analyzed and compared based on model fitting and the comparison of models. In model fitting, the best model obtained is Rv123-Joint-MLE, a model with a root nodes sequence of 123, and optimized using the joint maximum likelihood. The components for the best model are the Tawn type 1 and Rotated Tawn type 1 180 degrees representing the pair copulas of (intensity, duration), and (intensity, severity), respectively, with the Survival Gumbel for the conditional pair copula of (duration, severity; intensity). Based on the best model, the tri-variate dependence structure of the intensity, duration, and severity relationship is positively correlated, skewed, and follows an asymmetric distribution. This indicates that the characteristic’s, including intensity, duration, and severity, tend to increase together. Using comparison tests, the best model is significantly different from others, whereas only two models are quite similar. This shows that the best model is well-fitted, compared to most models. Overall, this paper highlights the capability of vine copula in modeling the asymmetric dependence structure of air pollution characteristics, where the obtained model has a better potential to become a tool to assess the risks of extreme events in future work.

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Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

Cited by 7 publications

References 59 publications

Multifractal Characteristics on Temporal Maximum of Air Pollution Series

Multifractal Characteristics on Temporal Maximum of Air Pollution Series

Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

Modeling Asymmetric Dependence Structure of Air Pollution Characteristics: A Vine Copula Approach

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