Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system

Shin, Uju; Park, Sang Hun; Park, Joon Sung; Koo, Ja Ho; Yoo, Changhyun; Kim, Soontae; Lee, Jae Bum

doi:10.1016/j.atmosenv.2020.118141

Cited by 10 publications

(5 citation statements)

References 48 publications

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“…Wind speed indicates the effect of the degree of dispersion on the accumulation or ventilation of airborne aerosols [4,30]. Relative humidity relates to the growth in the particle size and mass [24], and air temperature can be associated with the source of long-range transport [31] or the extent of precursor emissions from biogenic sources [32]. Visibility is another well-known indicator of aerosol pollutions [33].…”

Section: Methodsmentioning

confidence: 99%

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

et al. 2021

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Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation.

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Section: Methodsmentioning

confidence: 99%

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

et al. 2021

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“…DT was associated with a stronger circulation system as compared to DM (Figure S4 in Supporting Information ). A previous study suggested that the PM 2.5 concentration over Seoul is strongly associated with a blocking index based on geopotential height anomaly at 500 hPa over the East Sea and Japan (Shin et al., 2021). Moreover, this system shows a wave‐train pattern, which is a typical transient synoptic wave pattern over Korea during spring.…”

Section: Resultsmentioning

confidence: 99%

Relationship Between Synoptic Weather Pattern and Surface Particulate Matter (PM) Concentration During Winter and Spring Seasons Over South Korea

et al. 2022

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Regional air quality over East Asia, including South Korea, has recently become a center of public attention because of a few episodes of very high particulate matter (PM) concentrations. Predicting PM variation with lead time of a few hours up to days is one of the key areas that the governments are working on because it can benefit from an early warning system to short‐term mitigation effort. In this study, the influence of synoptic weather conditions on regional air quality was investigated with the occurrence frequencies of PM episodes as a function of various synoptic weather patterns during winter and spring. During winter, dry moderate (DM) types occur frequently alongside high PM cases (24 hr mean PM10 concentration >1000.25emnormalμnormalgm−3 $100\,{\upmu }\mathrm{g}{\mathrm{m}}^{-3}$). The composite weather map showed a weak northwesterly wind field as a potential cause. On the contrary, it is interesting to note that dry polar types can be associated with low PM cases (24 hr mean PM10 concentration <1000.25emnormalμnormalgm−3 $100\,{\upmu }\mathrm{g}{\mathrm{m}}^{-3}$) as well as high PM depending on near‐surface wind speed. Furthermore, DM and dry tropical types were found to be highly correlated with high (higher) PM concentrations during spring season, likely because of the enhanced static stability in the lower troposphere. It should be noted that PM concentration depends on the lower atmospheric stability. The close relationship between synoptic weather patterns and PM concentration suggests that synoptic weather can play an important role in regional air quality.

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“…Kim et al ( 2021 ) demonstrated how a numerical forecast of slowly varying atmospheric mode of variability, such as Arctic Oscillation, can be integrated into a statistical model to extend its surface air temperature forecast skill. Similarly, numerical forecasts of atmospheric blocking can be utilized to predict PM 10 concentrations several days ahead (Shin et al 2021 ).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Statistical Seasonal Forecasting of Winter and Spring PM2.5 Concentrations Over the Korean Peninsula

Jeong

Yoo

Yeh

et al. 2022

Asia-Pac J Atmos Sci

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Concentrations of fine particulate matter smaller than 2.5 μm in diameter (PM 2.5 ) over the Korean Peninsula experience year-to-year variations due to interannual variation in climate conditions. This study develops a multiple linear regression model based on slowly varying boundary conditions to predict winter and spring PM 2.5 concentrations at 1–3-month lead times. Nation-wide observations of Korea, which began in 2015, is extended back to 2005 using the local Seoul government’s observations, constructing a long-term dataset covering the 2005–2019 period. Using the forward selection stepwise regression approach, we identify sea surface temperature (SST), soil moisture, and 2-m air temperature as predictors for the model, while rejecting sea ice concentration and snow depth due to weak correlations with seasonal PM 2.5 concentrations. For the wintertime (December–January–February, DJF), the model based on SSTs over the equatorial Atlantic and soil moisture over the eastern Europe along with the linear PM 2.5 concentration trend generates a 3-month forecasts that shows a 0.69 correlation with observations. For the springtime (March–April–May, MAM), the accuracy of the model using SSTs over North Pacific and 2-m air temperature over East Asia increases to 0.75. Additionally, we find a linear relationship between the seasonal mean PM 2.5 concentration and an extreme metric, i.e., seasonal number of high PM 2.5 concentration days. Supplementary Information The online version contains supplementary material available at 10.1007/s13143-022-00275-4.

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Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system

Cited by 10 publications

References 48 publications

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

Relationship Between Synoptic Weather Pattern and Surface Particulate Matter (PM) Concentration During Winter and Spring Seasons Over South Korea

Statistical Seasonal Forecasting of Winter and Spring PM2.5 Concentrations Over the Korean Peninsula

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