• Journal of Korean Society of Environmental Engineers
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• v.32 no.8
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• pp.739-746
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• 2010

The purposes of this study are 1) to develop an advanced chamber system within ${\pm}10%$ of air velocity at the particulate matter (PM) collection area, 2) to research theoretical characteristics of PM10 and PM2.5 samplers, 3) to assess the performance characteristics of PM10 and PM2.5 samplers through chamber experiments. The total six one-hour experiments were conducted using the cornstarch with an mass median aerodynamic diameter (MMAD) of $20\;{\mu}m$ and an geometric standard deviation of 2.0 at the two different air velocity conditions of 0.67 m/s and 2.15 m/s in the chamber. The aerosol samplers used in the present study are one APM PM10 and one PM2.5 samplers accordance with the US federal reference methods and specially designed three mini-volume aerosol samplers (two for PM10 and one for PM2.5). The overall results indicate that PM10 and PM2.5 mini-volume samplers need correction factors of 0.25 and 0.39 respectively when APM PM samplers considered as reference samplers and there is significant difference between two mini-volume aerosol samplers when a two-way analysis of variance is tested using the measured PM10 mass concentrations. The PM10 and PM2.5 samplers with the cutpoints and slopes (PM10: $10{\pm}0.5\;{\mu}m$ and $1.5{\pm}0.1$, PM2.5: $2.5{\pm}0.2\;{\mu}m$ and $1.3{\pm}0.03$) theoretically collect the ranges of 86~114% and 64~152% considering the cornstarch characteristics used in this research. Furthermore, the calculated mass concentrations of PM samplers are higher than the ideal mass concentrations when the airborne MMADs for the cornstarch used are smaller than the cutpoints of PM samplers and the PM samplers collected less PM in another case. The chamber experiment also showed that PM10 and PM2.5 samplers had the bigger collection ranges of 37~158% and 55~149% than the theocratical calculated mass concentration ranges and the relatively similar mass concentration ranges were measured at the air velocity of 2.15 m/s comparing with the 0.67 m/s.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.657-664
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• 2021

In this study, we investigated the relationships between the air quality (PM_2.5, PM₁₀, O₃) concentrations and local geographical characteristics (terrain heights, building area ratios, population density in 9 km × 9 km gridded subareas) in the Seoul metropolitan area. To analyze the terrain heights and building area ratios, we used the geographic information system data provided by the NGII (National Geographic Information Institute). Also, we used the administrative districts and population provided by KOSIS (Korean Statistical Information Service) to estimate population densities. We analyzed the PM_2.5, PM₁₀, and O₃ concentrations measured at the 146 AQMSs (air quality monitoring system) within the Seoul metropolitan area. The analysis period is from January 2010 to December 2020, and the monthly concentrations were calculated by averaging the hourly concentrations. The terrain is high in the northern and eastern parts of Gyeonggi-do and low near the west coastline. The distributions of building area ratios and population densities were similar to each other. During the analysis period, the monthly PM_2.5 and PM₁₀ concentrations at 146 AQMSs were high from January to March. The O₃ concentrations were high from April to June. The population densities were negatively correlated with PM_2.5, PM₁₀, and O₃ concentrations (weakly with PM_2.5 and PM₁₀ but strongly with O₃). On the other hand, the AQMS heights showed no significant correlation with the pollutant concentrations, implying that further studies on the relationship between terrain heights and pollutant concentrations should be accompanied.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1691-1710
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• 2020

We measured PM2.5 and PM10 (particulate matter less than 2.5 ㎛ and 10 ㎛ in diameter, respectively) simultaneously at 16 locations around an elementary school and classrooms in Busan and Pyeongtaek, South Korea. In this study, we compared the results of this field intensive with those in the literature (144 cases of 30 studies), focusing on I/O (Indoor/Outdoor) ratios. We also reviewed the results of previous studies, categorizing them into related sub-categories for indoor-activities, seasons, building-uses, and the surrounding environment. We conclude that indoor PM10 is affected more by indoor-sources (e.g., physical activities) than PM2.5 in the absence of combustion sources like smoking and cooking. Additionally, PM10 and PM2.5 likely have different indoor-outdoor infiltration efficiencies. Conclusively, PM10 in classrooms can be more sensitively affected by both indoor activities and ambient concentrations, and mechanical ventilation can be more efficient in reducing PM concentrations than natural ventilation.

• Korean Journal of Poultry Science
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• v.48 no.3
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• pp.151-160
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• 2021

This study was conducted to determine the concentrations of particulate matter (PM) and NH3 emissions from different types of laying hens poultry houses during spring. The concentrations of PM and NH3 were measured three times (2-week intervals; March to May) in Floor-pen-, Aviary-, and Cage-type poultry houses. Overall, PM₁₀ and PM_2.5 concentrations were found to be low from 22:00 to 04:00. The PM₁₀ and PM_2.5 concentrations in Floor-pen and Cage houses were similar with no significant daily deviation. NH₃ concentrations measured over 24 h at the center and end of Floor-pen house were relatively constant. Irrespective of measurement location, NH₃ concentrations were the lowest in Floor-pen house. Moreover, NH₃ concentrations were higher at the end of Floor-pen and Aviary houses than that at the center; however, lower concentrations of NH₃ were detected at the end of Cage house. The concentrations of PM₁₀ and PM_2.5 around the poultry houses were 57.5 and 34.0 ㎍/m³, respectively, with the daily average PM₁₀ and PM_2.5 concentrations (4,730 and 447.7 ㎍/m³, respective) being the highest in Aviary house. The concentrations of NH₃ at the center and end of Cage house were the highest at 12.0 and 9.31 ppm, respectively. Furthermore, in Cage house, the emission factor of NH₃ was the lowest, whereas there was no significant difference on that of NH₃. In conclusion, among the three types of poultry houses assessed, PM (PM₁₀, PM_2.5) concentrations were higher in Aviary house, whereas NH3 concentrations were higher in Cage house.

• 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.24 no.3
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• pp.190-204
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• 2003

The PM (particulate matter) concentration data sets exceeding the Daily Air Quality Guidance Levels (i.e., established by the Korean Ministry of Environment) were selected from 31 air qality monitoring stations in Seoul from 1990 to 2000. (For reference, the 24hr environmental standard values of PM$_{10}$ and TSP are 150 and 300${\mu}$g/m$^{3}$, respectively.) When the data sets were compared between land use types, both PM fractions were exceeded most frequently in residential areas. However, the highest TSP concentration was measured at industrial areas (351.0${\pm}$35.9${\mu}$g m$^{3}$), while the highest PM$_{10}$ concentration was measured in residential areas (182.9${\pm}$42.4${\mu}$g m$^{3}$). When the temporal distribution patterns of the exceedance data sets were compared to those measured routinely (without any discrimination based on exceedance criteria), large differences were present. It was demonstrated that the occurrences of exceedance data sets increased rather significantly in recent years.

• Particle and aerosol research
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• v.7 no.2
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• pp.49-57
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• 2011

High concentration of PM10 was reported on late July, 2005 in Seoul along with high particulate ion concentrations in PM2.5. To identify the reason for the severe air pollution episode, time series analysis of the PM10 concentration in the monitoring sites over Korea, wind sector analysis, trend analysis of the ion concentrations, and air mass trajectory analysis were carried out. It was found that the episode could be classified into two separate periods; first one between July 22 and 27 and second one between July 28 and 31. During the first period, the PM10 concentrations at Seoul were in good correlation with the PM10 concentration three hours before at Chuncheon. Trajectory analysis showed that air mass moved from north and turned to west at Kangwon province to Seoul. The concentrations of PM10 mass and ionic species were lower than the second period. During the second period, air mass moved from northern China to Seoul directly and the PM10 concentrations all over the mid-Korean peninsula showed the same trend. These observations suggest that the air pollution during the second period was affected by direct transport of air pollutants from northern China. Thus, the air quality at Seoul during both periods were influenced by long-range transport from outside rather than by local sources, but with different transport patterns.

• 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.

• Korean Journal of Poultry Science
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• v.48 no.1
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• pp.23-30
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• 2021

In this study, we measured the concentration of particulate matter (PM) and ammonia (NH3) emission in the winters according to the breeding type of laying hen houses. Measurements were performed thrice in Barn, Aviary, and Cage houses every 2 weeks from December to January. The changes in the PM10 and PM2.5 concentrations were similar in all three breeding types. The PM10 and PM2.5 concentrations, measured three times, were the highest in the Aviary house. In the results measured by time, the PM10 and PM2.5 concentrations were the lowest during the dark period (22:00 to 4:00) of the day. The NH3 concentration was the highest in the Cage house and the lowest in the Barn house. Regarding emissions over time, the results of the three measurements showed different patterns and differed from those of the PM. In addition, with passage of time from the 1st (december) to 3rd (january), the NH3 concentration gradually increased. The daily PM10 and PM2.5 concentrations in the Aviary house, which were higher than those of the other houses, were 4,787 ㎍/㎥ and 388.6 ㎍/㎥, respectively, while, the PM10 and PM2.5 concentrations outside the poultry houses were 226.0 ㎍/㎥ and 39.3 ㎍/㎥, respectively. The daily NH3 concentration was 7.70 ppm and 9.20 ppm at the center and end of the Cage house, respectively. This was higher than that in the other houses. In conclusion, the concentrations of PM (PM10, PM2.5) and NH3 were the highest in the Aviary and Cage laying hen houses, respectively.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1117-1124
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• 2010

The PM10 (Promethium 10) data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly PM10 data at the southern part of the Gyeonggi-Do, Suwon monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables for the PM10 data set. The six meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, radiation, and amount of cloud. The four air pollution explanatory variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result showed that the monthly ARE models explained about 13-49% for describing the PM10 concentration.