• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.569-575
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• 2021

This study analyzed the effect of forests on reducing particulate matter by investigating the particulate matter concentration and influencing factors between urban forest and traffic forest. The concentrations of particulate matter in Hongreung Experimental Forest (urban forest) and a forest (traffic forest) formed at the intersection of Cheongryangri Station in Dongdaemun-gu, Seoul were measured with the light scattering method instrument from January to November 2018. During the study period, the average PM₁₀ concentrations in the urban forest and the traffic forest were 12.5㎍/m³ and 15.7 ㎍/m³, respectively, and the average PM_2.5 concentrations were 16.6㎍/m³and 6.9 ㎍/m³, respectively. Comparing the concentration by the urban atmospheric measurement network of the Ministry of Environment and the concentration in urban forests showed that the reduction rate of PM₁₀ was 66.9±28.6% in urbanforest and 58.6±44.1% in traffic forest and that of PM_2.5 was 71.3±23.0% and 64.9±31.3%. The difference in the reduction rate of particulate matter is likely related to the size and structure of the urban forest, and the wind velocity is considered the reduction factor.

• Journal of Environmental Impact Assessment
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• v.30 no.4
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• pp.237-246
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• 2021

The similarity of hourly PM₁₀ and PM_2.5 concentration profiles of the atmospheric monitoring stations in Gyeonggi-do was evaluated through the multilateral analysis between stations. The existing category for most stations in the regions shows relatively low Pearson correlation values of 0.68 and 0.7 for PM₁₀ and PM_2.5 on average respectively, and some monitoring stations revealed high relationships over 0.8 to other regions. Since the current regions are mainly categorized by cluster analysis based on the number of occurrence of high concentration events and geological factors, it is necessary to reclassify them by concentration characteristics for precise fine dust management. In accordance, multi-dimensional scaling being able to visualize could categorize the regions based on regional emission contribution rate and hourly fine dust concentration. As a result of the current analysis, PM₁₀ and PM_2.5 could be reclassified into five regions and fourregions, respectively.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• Journal of Environmental Impact Assessment
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• v.28 no.6
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• pp.568-579
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• 2019

The present study aims to identify the effect of land cover categories on particulate matter (PM) concentrations by analyzing the correlation between monthly PM concentrations in Seoul's air quality monitoring network and the percentages of land cover categories by buffers around air quality monitoring stations. According to a monthly correlation analysis between land cover categories and PM concentrations, in the buffer 3km, PM₁₀ showed a better correlation than PM_2.5, there was a clear negative correlation with the forest area, the grassland and the urbanized area had some positive correlation with PM₁₀, and the barren land and the urbanized area had some positive correlation with PM_2.5. According to a monthly correlation analysis of dominant land cover sub-categories and sub-sub-categories within the buffer 3km, PM₁₀ showed a clear negative correlation with the broad-leaved forest, and some positive correlation with the road was dominant. PM_2.5 showed partly negative correlation with the broad-leaved forest and partly positive correlation with the commercial area. There was a very low or no correlation with other grassland and bare land subcategories. A monthly stepwise regression analysis on noticeable land cover sub-categories and sub-sub-categories with positive or negative correlations revealed that an increasing percentage of the broad-leaved forest had a clear effect on reducing PM₁₀ concentrations, and the road was excluded from the selected variables. Although an increasing percentage of the commercial area had some effect on increasing monthly PM_2.5 concentrations and an increasing percentage of the broad-leaved forest had an effect on decreasing the PM_2.5 concentrations, their effect size was smaller than that on PM₁₀. The forest area around the city center had the largest and clearest effect on reducing PM concentrations. The urbanized area's sub-categories and sub-sub-categories were also confirmed to have some effect on increasing PM concentrations.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.1-18
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• 2014

The purpose of this study was to analyze the sensitivity of meteorological fields and the variation of concentration of particulate matters (PMs) due to aerosol schemes and dust options within the WRF-Chem model to estimate Asian dusts affected on 29 May 2008 in the Korean peninsula. The anthropogenic emissions within the model were adopted by the $0.5^{\circ}{\pm}0.5^{\circ}$ RETRO of the global emissions, and the photolysis option was by Fast-J photolysis. Also, three scenarios such as the RADM2 chemical mechanism and MADE/SORGAM aerosol, the MOSAIC 8 section aerosol, and the GOCART dust erosion were simulated for calculating Asian dust emissions. As a result, the scenario of the RADM2 chemical mechanism & MADE/SORGAM aerosol depicted higher concentration than the others' in both Asian dusts and the background concentration of PMs. By comparing of the daily mean of PM10 measured at each air quality monitoring site in Seoul with the scenario results, the correlation coefficient was 0.67, and the root mean square error was $44{\mu}gm^{-3}$. In addition, the air temperature, the wind speed, the planetary boundary layer height, and the outgoing long-wave radiation were simulated under conditions of no chemical option with these three scenarios within the WRF or WRF-Chem model. Both the spatial distributions of the PBL height and the wind speed of u component among the meteorological factors were similar to those of the Asia dusts in range of 1,800-3,000 m and $2-16ms^{-1}$, respectively. And, it was shown that both scenarios of the RADM2 chemical mechanism and MADE/SORGAM aerosol and the GOCART dust erosion were interacted on-line between meteorological factors and Asian dusts or aerosols within the model because the outgoing long-wave radiation was changed to lower than the others.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5601-5609
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• 2012

The fine particulate matter($PM_{10}$) concentrations and contents were measured to check the health and environment influential factors in Pohang Iron and Steel Industrial Complex and its vicinities. In addition, the $PM_{10}$ distribution for each year and season was surveyed using the regional air quality monitoring stations. The measuring on the $PM_{10}$ inside the industrial complex showed $61.3{\pm}12.1{\mu}g/m^3$ for average concentration of $PM_{10}$ which was measured by Dongil Industry and $44.3{\pm}8.1{\mu}g/m^3$ measured by steel manufacturing industry complex management office. Both of them satisfied the environmental air quality standard. The percentage of $SO_4{^2}$, $NO_3{^-}$, $NH_4{^+}$ which are the secondary ions created out of the $PM_{10}$ in Dongil Industry and steel manufacturing industry complex management office was checked and it was revealed that the percentage of ${SO_4}^{2-}$ was high and it is considered that the pollution source related with the sulfides exist at the industrial complex. They were in order of ${SO_4}^{2-}$ > $Cl^-$ > $NO_3{^-}$ > $F^-$ > $NH_4{^+}$ in Dongil Industry and ${SO_4}^{2-}$ > $Cl^-$ > $NO_3{^-}$ > $NH_4{^+}$ > $F^-$ in steel manufacturing industry complex management office.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.2
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• pp.133-140
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• 2009

In order to examine the effects of micrometeorological and climatological influences on urban scale particulate air pollutants observed in Busan, power spectrum analysis was applied to the observed particulate matter with aerodynamic diameter ${\le}10{\mu}m$ ($PM_{10}$) for the period from 1991 to 2006. Power spectrum analysis has been employed to the daily mean $PM_{10}$ concentrations obtained at 13 sites to identify different scales of periodicities of $PM_{10}$ concentrations. The results show that, aside from the typical and well-known periodicities such as diurnal and annual variations caused by anthropogenic emission influences, another two significant peaks of power spectrum density were identified: 21 day and $3{\sim}4$ year of periodicities. Cospectrum analysis indicates that the intraseasonal 21 day periodicity are found to be negatively correlated with wind speed and surface pressure but shows consistently positive with relative humidity and temperature. This result implied that 21 day periodicity is presumably relevant to the secondary aerosol formation processes through the photochemical reaction that can be subsequently resulted from hygroscopic characteristics of aerosol formation. However, the interannual $3{\sim}4$ year of periodicity is found to have positive correlation with pressure, and negative with temperature and relative humidity, which is rather consistent with both characteristics of air mass during the Asian dust event and the occurrence frequency of Asian dust whose periodicities have been recorded inter-annually over the Korean peninsula.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.