Season-Resolved, Fluctuation-Level Regional Connectivity of PM2.5 over the Korean Peninsula Revealed by Multifractal Detrended Cross-Correlation Networks (2016–2020)

Motivated by the strong seasonality of East Asian meteorology and its control on pollution episodes characterized by fluctuation level, we model the season-resolved climatology of the regional PM2.5 connectivity over the Korean Peninsula. Using daily AirKorea data for 2016–2020, we (i) remove daily climatology and the peninsula-wide background (empirical orthogonal function; EOF1) to obtain residual signals; (ii) compute the sign-preserving multifractal detrended cross-correlation coefficient MFDCCA-ρq,s; (iii) apply iAAFT surrogate significance across scales; and (iv) construct signed, weighted networks aggregated over short (5–15 d) and mid (15–30 d) bands for DJF/MAM/JJA/SON. Our analysis targets the seasonal climatology of fluctuation-level (q-dependent) connectivity by pooling seasons across years; this approach increases statistical robustness at 5–30-day scales and avoids diluting season-specific organization. We find negligible connectivity for q<0 (small fluctuations) but dense, seasonally organized networks for q>0 (strongest in winter–spring and at 15–30 days). After removing the EOF1, positive subgraphs form assortative regional backbones, while negative subgraphs reveal a northwest–southeast anti-phase dipole; the connectivity around Baengnyeongdo (B) highlights a transboundary sentinel role in cool seasons. These results demonstrate that a season-resolved, fluctuation-level framework effectively isolates regional connectivity that would otherwise be masked in annual aggregates or by the peninsula-wide background.

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Publication Details

Journal
Fractal and Fractional
Published
2025-11-14
DOI
https://doi.org/10.3390/fractalfract9110737
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
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article

Season-Resolved, Fluctuation-Level Regional Connectivity of PM2.5 over the Korean Peninsula Revealed by Multifractal Detrended Cross-Correlation Networks (2016–2020)

Gyuchang Lim, Seungsik Min
Fractal and Fractional
Climate variability and models
article

Season-Resolved, Fluctuation-Level Regional Connectivity of PM2.5 over the Korean Peninsula Revealed by Multifractal Detrended Cross-Correlation Networks (2016–2020)

Gyuchang Lim, Seungsik Min
article en

Abstract

Motivated by the strong seasonality of East Asian meteorology and its control on pollution episodes characterized by fluctuation level, we model the season-resolved climatology of the regional PM2.5 connectivity over the Korean Peninsula. Using daily AirKorea data for 2016–2020, we (i) remove daily climatology and the peninsula-wide background (empirical orthogonal function; EOF1) to obtain residual signals; (ii) compute the sign-preserving multifractal detrended cross-correlation coefficient MFDCCA-ρq,s; (iii) apply iAAFT surrogate significance across scales; and (iv) construct signed, weighted networks aggregated over short (5–15 d) and mid (15–30 d) bands for DJF/MAM/JJA/SON. Our analysis targets the seasonal climatology of fluctuation-level (q-dependent) connectivity by pooling seasons across years; this approach increases statistical robustness at 5–30-day scales and avoids diluting season-specific organization. We find negligible connectivity for q<0 (small fluctuations) but dense, seasonally organized networks for q>0 (strongest in winter–spring and at 15–30 days). After removing the EOF1, positive subgraphs form assortative regional backbones, while negative subgraphs reveal a northwest–southeast anti-phase dipole; the connectivity around Baengnyeongdo (B) highlights a transboundary sentinel role in cool seasons. These results demonstrate that a season-resolved, fluctuation-level framework effectively isolates regional connectivity that would otherwise be masked in annual aggregates or by the peninsula-wide background.

Fractal and FractionalVol. 9(11)
Korea Naval Academy (KR), Kyungpook National University (KR)
Openalex Percentile: Top 68%
Climate variability and models
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