• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Particle and aerosol research
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• v.19 no.1
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• pp.13-20
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• 2023

The Asian dust observation network operates β-ray attenuation samplers to measure PM₁₀ concentrations. In addition, equivalence evaluation and accuracy inspection(Precision Tests) are conducted every year for the reliability of data. β-ray attenuation samplers(16 units) were comparatively observed from May to June 2020 and from July to December 2021. During the observation period, the average daily temperature was the lowest at 6.4℃ in December and the highest at 27.3℃ in August. The average daily humidity ranged from 60% to 100%, but the average daily humidity was over 75% from July to September. The minimum value of the PM₁₀ Gravimetric method was 5.0 ㎍/m³, the maximum value was 53.4 ㎍/m³, and the average value was 17.8 ㎍/m³. The equivalence evaluation results of the PM₁₀ Gravimetric method and β-ray attenuation samplers satisfied the criteria (slope: 1±0.1, intercept: 0±0.5). A relative error analysis between the PM₁₀ Gravimetric method and β-ray attenuation samplers equipment showed that the relative error increased when the concentration was low and the temperature and humidity were high. In addition, in the β-ray attenuation samplers 5-minute interval observation data in May 2020, a relatively large Standard devication was shown as an average maximum ±23.4 ㎍/m³ and a minimum ±15.2 ㎍/m³. At standard deviations of 10% and 90%, equipment with high variability (deviation) was measured at 6 ㎍/m³and 61 ㎍/m³, and equipment with low variability was measured at 12 ㎍/m³ and 47 ㎍/m³. It was confirmed that concentration differences occurred due to differences in variability for each equipment.

• Journal of Environmental Science International
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• v.25 no.7
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• pp.1029-1042
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• 2016

The initial and boundary conditions are important factors in regional chemical transport modeling systems. The method of generating the chemical boundary conditions for regional air quality models tends to be different from the dynamically varying boundary conditions in global chemical transport models. In this study, the impact of real time Copernicus atmosphere monitoring service (CAMS) re-analysis data from the modeling atmospheric composition and climate project interim implementation (MACC) on the regional air quality in the Korean Peninsula was carried out using the community multi-scale air quality modeling system (CMAQ). A comparison between conventional global data and CAMS for numerical assessments was also conducted. Although the horizontal resolution of the CAMS re-analysis data is not higher than the conventionally provided data, the simulated particulate matter (PM) concentrations with boundary conditions for CAMS re-analysis is more reasonable than any other data, and the estimation accuracy over the entire Korean peninsula, including the Seoul and Daegu metropolitan areas, was improved. Although an inland area such as the Daegu metropolitan area often has large uncertainty in PM prediction, the level of improvement in the prediction for the Daegu metropolitan area is higher than in the coastal area of the western part of the Korean peninsula.

• Atmosphere
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• v.22 no.1
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• pp.73-85
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• 2012

The collision efficiency data for collision between graupel or hail particles and cloud drops that take into account the differences of particle density are applied to the Takahashi cloud model. The original setting assumes that graupel or hail collision efficiency is the same as that of the cloud drops of the same volume. The Takahashi cloud model is run with the new collision efficiency data and the results are compared with those with the original. As an initial condition, a thermodynamic profile that can initiate strong convection is provided. Three different CCN concentration values and therefore three initial cloud drop spectra are prescribed that represent maritime (CCN concentration = 300 $cm^{-3}$), continental (1000 $cm^{-3}$) and extreme continental (5000 $cm^{-3}$) air masses to examine the aerosol effects on cloud and precipitation development. Increase of CCN concentration causes cloud drop sizes to decrease and cloud drop concentrations to increase. However, the concentration of ice particles decreases with the increase of CCN concentration because small drops are difficult to freeze. These general trends are well captured by both model runs (one with the new collision efficiency data and the other with the original) but there are significant differences: with the new data, the development of cloud and raindrop formation are delayed by (1) decrease of ice collision efficiency, (2) decrease of latent heat from riming process and (3) decrease of ice crystals generated by ice multiplication. These results indicate that the model run with the original collision efficiency data overestimates precipitation rates.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.1
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• pp.93-100
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• 2003

In this technical information, the long-term measurement data at Gosan between 1992 and 2001 are analyzed with various statistical methods. First. it was confirmed that the basic assumption of t-test is important to classify data correctly. Second, it was founded that the difference of the number of data per month can affect the averaged concentration. Third, by using a non-parametric statistical method long term trend of aerosol composition free from seasonal effects is obtained.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.1-18
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• 2024

This study analyzed the effect of warming on PM_2.5 aerosol production in mid-latitude East Asia during June 2020 using PM_2.5 aerosol anomalies, which were identified by incorporating meteorological and climate data into the Weather Research Forecasting model coupled with Chemistry (WRF-Chem) model. The decadal temperature change trend over a 30-year period (1991-2020) in East Asia showed that recent warming has been greater in summer than in winter. Summer warming in East Asia generated low and high pressure in the lower and upper troposphere, respectively, over China. The boundary between the lower tropospheric low and upper tropospheric high pressure sloped along the terrain from the Tibetan Plateau to Korea. The eastern China, Yellow Sea, and Korean regions experienced a convergence of warm and humid southwesterly airflows originating from the East China Sea with the development of a northwesterly Pacific high pressure. In June 2020, the highest temperatures were observed since 1973 in Korea. Meanwhile, enhanced warming in East Asia increased the production of PM_2.5 aerosols that travelled long distances from eastern China to Korea. PM_2.5 anomalies, which were derived solely by inputting meteorological and climatic data (1991-2020) into the WRF-Chem model and excluding emission variations, showed a positive distribution extending from eastern China to South Korea across the Yellow Sea as well as over the Pacific Northwest. Thus, the contribution of warming to PM_2.5 aerosols in East Asia during June 2020 was more than 50%. In particular, PM_2.5 aerosols were transported from eastern China to Korea through the Yellow Sea, where the warm and humid southwesterly airflows implied wet scavenging of sulfate but promoted nitrate production.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Aerospace Engineering and Technology
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• v.13 no.2
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• pp.80-86
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• 2014

In this study, we the estimated surface normalized difference vegetation index by using the KOrea Multi-Purpose SATellite-3 (KOMPSAT-3) multi-spectral images for comparative analysis. The estimated NDVI from KOMPSAT-3 is used as for comparison with the high resolution GeoEye products. The geometry conditions for atmospheric effects are selected from meta files of KOMPSAT-3 bundle data. The used geometry conditions are consist of solar zenith angle, solar azimuth angle, viewing zenith angle, viewing azimuth angle, and date. And, Atmospheric effects such as attenuation, scattering and absorption were physically simulated from water vapor, ozone and aerosol information. Generally, although ground measurements are important for accurate information, in this study, MODIS atmospheric products are used as atmospheric constituents. The surface reflectance from radiative transfer model is utilized for estimating vegetation index. The present study, to reduce atmospheric and geometry conditions between KOMPSAT-3 and GeoEye having difference observation characteristics, data acquisition time is carefully determined for reliable vegetation spectral characteristics.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.4
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• pp.285-295
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• 2002

In this study, several types of gravimetric methods (such as high, medium, low, and ultra low volume sampling methods) were applied to determine suspended particulate matter concentrations in both ambient and indoor environments. Comparative evaluations were undertaken with SPM data obtained using a variety of samplers (TSP, PM10, and PM4.0) at different sampling flow rates. Correlation coefficients between TSP and PM10 concentrations measured at different flow rates fell in the range of 0.73∼0.94 (n=40). In addition, correlation coefficients for PM concentrations measured by different TSP samplers were in the range of 0.90∼0.95 (n=36 or n=38), while 0.77∼0.91 (n=38) for PM10 samplers. Correlation analysis was also conducted on indoor monitoring data that were measured using ultra-low-volume samplers at both different or identical flow rates. The correlation coefficients were in the range of 0.98∼0.99 (n=38) between TSP and TSP and 0.92∼0.94 (n=38) between TSP and PM10. The mean ratio for high volume PM10 to TSP concentration that was monitored at identical flow rates in the ambient air appeared to be 0.72. The mean ratios of PM10 to TSP and PM4.0 to TSP observed with identical flow rates at indoor environments were 0.47 and 0.40. The results of this study may provide empirical information concerning the compatability of aerosol data obtained by gravimetric sampling methods at different flow rates.

• Atmosphere
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• v.18 no.4
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• pp.267-277
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• 2008

The occurrence and strength of the Asian Dust over the Korea Peninsular have been increased by the expansion of the desert area. For the continuous monitoring of the Asian Dust event, the geostationary satellites provide useful information by detecting the outbreak of the event as well as the long-range transportation of dust. The Infrared Optical Depth Index (IODI) derived from the MTSAT-1R data, indicating a quantitative index of the dust intensity, has been produced in real-time at Korea Meteorological Administration (KMA) since spring of 2007 for the forecast of Asian dust. The data processing algorithm for IODI consists of mainly two steps. The first step is to detect dust area by using brightness temperature difference between two thermal window channels which are influenced with different extinction coefficients by dust. Here we use dynamic threshold values based on the change of surface temperature. In the second step, the IODI is calculated using the ratio between current IR1 brightness temperature and the maximum brightness temperature of the last 10 days which we assume the clear sky. Validation with AOD retrieved from MODIS shows a good agreement over the ocean. Comparison of IODI with the ground based PM10 observation network in Korea shows distinct characteristics depending on the altitude of dust layer estimated from the Lidar data. In the case that the altitude of dust layer is relatively high, the intensity of IODI is larger than that of PM10. On the other hand, when the altitude of dust layer is lower, IODI seems to be relatively small comparing with PM10 measurement.