• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.389-391
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• 2008

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.215-231
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• 2018

It was analyzed to identify the cause of $PM_{2.5}$ concentration changes for the fall in 2016 and 2017 in South Korea using ground measurement data such as meterological variables and $PM_{2.5}$, AOD from GOCI satellite, and WRF-CMAQ modeling system. The result of ground measurement data showed that the $PM_{2.5}$ concentrations for the fall in 2017 decreased by 12.3% ($3.0{\mu}g/m^3$) compared to that of 2016. The difference of $PM_{2.5}$ concentrations between 2016 and 2017 mainly occurred for 11 Oct. - 20 Oct. (CASE1) and 15 Nov. - 19 Nov. (CASE2) when weather conditions were difficult to long-range transport from foreign regions and favored atmospheric ventilation in 2017 compared to 2016. Simulated $PM_{2.5}$ concentrations in 2017 decreased by 64.0% ($23.1{\mu}g/m^3$) and 35.7% ($12.2{\mu}g/m^3$) during CASE1 and CASE2, respectively. These results corresponded to the changes in observed $PM_{2.5}$ concentrations such as 53.6% for CASE1 and 47.8% for CASE2. It is implied that the changes in weather conditions affected significantly the $PM_{2.5}$ concentrations for the fall between 2016 and 2017. The contributions to decreases in $PM_{2.5}$ concentrations was assessed as 52.8% by long-range transport from foreign regions and 47.2% by atmospheric ventilation effects in domestic regions during CASE1, whereas their decreases during CASE2 were affected by 66.4% from foreign regions and 33.6% in domestic regions.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.2
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• pp.112-121
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• 2006

To evaluate the performance of ventilation system properly, the correlations among the ventilation rate, indoor air-quality and cooling/heating load should be analysed. In this study, simulation tool to analyze the performance of ventilation system was developed. The simulation tool is based on the TRNSYS and some modules to calculate concentration of pollutants with the operation of ventilation system and to decide the signal of ventilation system were newly developed in this study. And these modules coupled with building load and heating/cooling simulation modules. To verify the validity of developed simulation tool, comparison study between simulation and field study were accomplished. As results, the simulation tool developed in this study can be used to predict the performance of ventilation system with accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.21-28
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• 2010

Air pollution monitoring has attracted a lot of interests because it affects directly to the human life quality. The most of the current air pollution monitoring stations use the expensive and bulky instruments and are only installed in the specific area. Therefore, it is difficult to install them to as many places as people need. In this work, we constructed a low price and small size Radio Frequency(RF) sensor system to solve this problem. This system also had the measurement range similar to the ones used in the air pollution forecast systems. This system had the sensor unit to measure the air quality, the central processing unit for air quality data acquisition, the power unit to supply the power to every units, and the RF unit for the wireless transmission and reception of the data. This system was easy to install in the field. We also added a GPS unit to track the position of the RF-sensor in real time by wireless communication. For the various measurements of the air pollution, we used CO, $O_3$, $NO_2$ sensors as gas sensors and also installed a dust sensor.

• Journal of Environmental Science International
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• v.29 no.8
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• pp.847-855
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• 2020

This study presented problems and improvements in the quality control of an air monitoring network, using Gyeongnam as an example. 1) The effective utilization rate of the air monitoring was 95%, which showed good management, but the maximum of 2% was indicated by zero or detection limit among measurement data. 2) In the equivalence evaluation of PM_2.5, the slope and intercept satisfy the evaluation criteria; however, 1% of the PM_2.5/PM₁₀ ratios were outliers. 3) All air monitoring stations meet the quality control standards; however, the management status is added to the quality inspection, management system is unified and the related budget is expanded, and systematic commission management is required.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.5
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• pp.410-419
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• 2009

Generally, air pollutant emission at workplace is estimated by two methods: indirect methods using emission factors and direct methods based on CEMS (Continuous Emission Monitoring System). CAPSS (Clean Air Policy Support System) is a representative indirect method and the national air pollutant database of Korea. However, characteristics of some workplaces may create a gap between CAPSS and CEMS data. For improving of emission data accuracy, emission data of CEMS (named CleanSYS) equipped at 138 target workplaces were compared with those of CAPSS. As a result, $SO_x$ and $PM_{10}$ emission levels obtained by CAPSS were lower than those of CleanSYS. $SO_x$ and $PM_{10}$emission ratios were 61.5% and 71.2% lower respectively, showing the biggest gaps. On the other hand, $NO_x$ emission of CAPSS was higher by 10.4%. $SO_x$ showed the biggest difference in 'Energy industry combustion' and $NO_x$ did in 'Production Process' within the SCC category. $PM_{10}$ presented a large gap in 'Manufacturing industry combustion.' The differences in $SO_x$ between the two systems occurred because some large-size facilities lack pollution controllers or efficient pollution controllers. Based on this study, CAPSS emission database of Korea will improve accuracy through adopting CEMS emission system, which enables more efficient national atmospheric policies and workplace management.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.3
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• pp.284-299
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• 2008

Emission input modules was developed to produce emission input data and change some profiles for Sparse Matrix Operator Kernel Emissions (SMOKE) using Clean Air Policy Support System (CAPSS)'s activities and previous studies. Specially, this study was focused to improve chemical speciation and temporal allocation profiles of SMOKE. At first, SCC cord mapping was done. 579 SCC cords of CAPSS were matched with EPA's one. Temporal allocation profiles were changed using CAPSS monthly activities. And Chemical speciation profiles were substituted using Kang et al. (2000) and Lee et al. (2005) studies and Kim et al. (2005) study. Simulation in Seoul Metropolitan Area (Seoul, Incheon, Gyeonggi) using MM5, SMOKE and CMAQ modeling system was done for effect analysis of changed input modules of SMOKE. Emission model results adjusted with new input modules were slightly changed as compared to using EPA's default modules. SMOKE outputs shows that aldehyde emissions were decreased 4.78% after changing chemical profiles, increased 0.85% after implementing new temporal profiles. Toluene emissions were decreased 18.56% by changing chemical speciation profiles, increased 0.67% by replacing temporal profiles as well. Simulated results of air quality were also slightly elevated by using new input modules. Continuous accumulation of domestic data and studies to develop input system for air quality modeling would produce more improved results of air quality prediction.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.165-170
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• 2024

This study aims to verify the accuracy of the air quality management system in Yangju City using an artificial intelligence (AI) evaluation model. The consistency and reliability of fine dust data were assessed by comparing public data from the Ministry of Environment with data from Yangju City's air quality management system. To this end, we analyzed the completeness, uniqueness, validity, consistency, accuracy, and integrity of the data. Exploratory statistical analysis was employed to compare data consistency. The results of the AI-based data quality index evaluation revealed no statistically significant differences between the two datasets. Among AI-based algorithms, the random forest model demonstrated the highest predictive accuracy, with its performance evaluated through ROC curves and AUC. Notably, the random forest model was identified as a valuable tool for optimizing the air quality management system. This study confirms that the reliability and suitability of fine dust data can be effectively assessed using AI-based model performance evaluation, contributing to the advancement of air quality management strategies.

• Journal of odor and indoor environment
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• v.17 no.4
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• pp.346-354
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• 2018

In this study, the condition of the hazardous materials in the bus was monitored according to the ventilation mode of the air conditioning system during bus service. The bus was surveyed using the indoor air quality measurement method of public transportation vehicles within one year of delivery. We evaluate the $CO_2$ and $PM_{10}$, which are the controlled parameters in buses by the Ministry of Environment, and VOCs and HCHO, the non-controlled parameters. The $PM_{10}$ concentration increased due to outdoor air intake; however the $CO_2$ concentration was found to decrease. In addition, the concentration of VOCs and HCHO was found to decrease due to the forced ventilation system and the outdoor air intake. These results show that the concentration of the other materials except $PM_{10}$ can be changed due to the outside air concentration and forced ventilation system. Therefore, through indoor air quality characteristics of the bus according to air condition system are intended to be used as the basis of an operation manual.

• Tuberculosis and Respiratory Diseases
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• v.86 no.1
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• pp.23-32
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• 2023

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.