Abstract
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.
Highlights
If we let ζ u = P( X > u) = nk, where k is the number of data points xi exceeding the threshold u, the return period presented in Equation (22) can be simplified as: P (Y > y ) = ζ u y−u
This step implies that the extreme data points selected by the mixed peak-over-threshold– block-maxima (POT-BM) approach can satisfy or at least satisfactorily approximate the properties of an independent variable
To provide a better assessment, we suggest that the generalized extreme-value (GEV) (POT-BM) model needs to be re-fitted with a current set of data to obtain the most up-to-date short term (5 or 10 years of the future) evaluation of extreme pollution event over time
Summary
Klang, which is located in Peninsular Malaysia at a latitude of 101◦ 260 44.023 E and a longitude of 3◦ 20 41.701 N, is one of Malaysia’s largest cities, with a land area of approximately 573 km. O3 × 1000, i f O3 < 0.2 ppm, the O3 pollutant can be computed using the following equaThe sub-API value for. The sub-API value for the NO2 pollutant can be computed using the following equation: NO2 × 588.23529, if NO2 < 0.17 ppm, 232.56. 0.04 ≤ SO2 < 0.3 ppm, The Idx sub-API for the SO2 pollutant can be computed using the SO2 = value (4). The higher the API value at a particular time, the greater the threat of the occurrence of extreme pollution events. Such scenarios disrupt the economic activities of the country, lead to a high risk health problems among populations, and negatively affect environmental ecosystems. This study was interested in the occurrence of extreme air pollution events determined by unhealthy API indices
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