Hierarchical-Generalized Pareto Model for Estimation of Unhealthy Air Pollution Index

AL-Dhurafi, Nasr Ahmed; Masseran, Nurulkamal; Zamzuri, Zamira Hasanah

doi:10.1007/s10666-020-09696-9

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Thus, to measure the API indices, these pollutant variables need to be standardized to derive individual indices. 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].…”

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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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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“…In addition, the stochastic dependence of API data in Klang was also investigated using a discrete-time Markov chain model and concluded that the occurrence of unhealthy events is relatively small, but that these events are quite troubling [20]. 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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“…The existence of such rare events can affect data distribution characteristics, such as in the form of high skewness and kurtosis, which correspond to long-and heavy-tail behaviors. In environmental phenomena, the occurrence of extreme events can be observed in various fields, such as extreme precipitation and flooding [1], extreme wind speeds [2], droughts [3], extreme temperatures [4], natural hazards [5], and air pollution events [6,7].…”

Section: Introductionmentioning

confidence: 99%

Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

Masseran

Safari

2021

IJERPH

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This article proposes a novel data selection technique called the mixed peak-over-threshold–block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular threshold u. The selected groups are defined as POT blocks. In parallel with that, a declustering technique is used to overcome the problem of dependency behaviors that occurs among adjacent POT blocks. Finally, the BM concept is integrated to determine the maximum data points for each POT block. Results show that the extreme data points determined by the mixed POT-BM approach satisfy the independent properties of extreme events, with satisfactory fitted model precision results. Overall, this study concludes that the mixed POT-BM approach provides a balanced tradeoff between bias and variance in the statistical modeling of extreme-value events. A case study was conducted by modeling an extreme event based on unhealthy air pollution events with a threshold u > 100 in Klang, Malaysia.

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Hierarchical-Generalized Pareto Model for Estimation of Unhealthy Air Pollution Index

Cited by 8 publications

References 23 publications

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

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

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

Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

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