• Journal of Environmental Science International
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• v.27 no.8
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• pp.723-735
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• 2018

In this study, to investigate an optimal configuration method for the modeling system, we performed an optimization experiment by controlling the types of compilers and libraries, and the number of CPU cores because it was important to provide reliable model data very quickly for the national air quality forecast. We were made up the optimization experiment of twelve according to compilers (PGI and Intel), MPIs (mvapich-2.0, mvapich-2.2, and mpich-3.2) and NetCDF (NetCDF-3.6.3 and NetCDF-4.1.3) and performed wall clock time measurement for the WRF and CMAQ models based on the built computing resources. In the result of the experiment according to the compiler and library type, the performance of the WRF (30 min 30 s) and CMAQ (47 min 22 s) was best when the combination of Intel complier, mavapich-2.0, and NetCDF-3.6.3 was applied. Additionally, in a result of optimization by the number of CPU cores, the WRF model was best performed with 140 cores (five calculation servers), and the CMAQ model with 120 cores (five calculation servers). While the WRF model demonstrated obvious differences depending on the number of CPU cores rather than the types of compilers and libraries, CMAQ model demonstrated the biggest differences on the combination of compilers and libraries.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.203-210
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• 2019

Recently, a number of researchers have produced research and reports in order to forecast more exactly air quality such as particulate matter and odor. However, such research mainly focuses on the atmospheric diffusion models that have been used for the air quality prediction in environmental engineering area. Even though it has various merits, it has some limitation in that it uses very limited spatial attributes such as geographical attributes. Thus, we propose the new approach to forecast an air quality using a deep learning based ensemble model combining temporal and spatial predictor. The temporal predictor employs the RNN LSTM and the spatial predictor is based on the geographically weighted regression model. The ensemble model also uses the RNN LSTM that combines two models with stacking structure. The ensemble model is capable of inferring the air quality of the areas without air quality monitoring station, and even forecasting future air quality. We installed the IoT sensors measuring PM2.5, PM10, H2S, NH3, VOC at the 8 stations in Jeonju in order to gather air quality data. The numerical results showed that our new model has very exact prediction capability with comparison to the real measured data. It implies that the spatial attributes should be considered to more exact air quality prediction.

• Journal of Environmental Health Sciences
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• v.23 no.4
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• pp.57-66
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• 1997

Successful energy conservation and good indcfor air quality (IAQ) are highly dependent on ventilation system. Air filtration is a primary solution of indoor air control strategies in terms of reducing energy consumption and improving ihdoor air quality. A conventional system with bypass filter, as it is called variable-air-volume/bypass filtration system (VAV/BPFS), is a variation of the conventional variable air volume (VAV) systems, which is designed to eliminate indoor air pollutant and to save energy. Bypass filtration system equipped with a high-efficiency particulate filter and carbon absorbent provides additional cleaned air into indoor environments and maintain good IAQ for human health. The objectives of this research were to compare the relative total decay rate of indoor air pollutant concentrations, and to develop a mathematical model simulating the performance of VAV/BPFS. All experiments were performed in chamber under the controlled conditions. The specific conclusions of this research are: 1. The VAV/BPFS system is more efficient than the VAV system in removing indoor air pollutant concentration. The total decay rates of aerosol, and total volatile organic compound (TVOC) for the VAV/BPFS system were higher than those of the conventional VAV system. 2. IAQ model predictions of each pollutant agree closely with the measured values. 3. According to IAQ model evaluation, reduction of outdoor supply air results in decreased dilution removal rate and on increased bypass filtration removal rate with the VAV/BPFS. As a results, we recommends the VAV/BPFS as an alternative to conventional VAV systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.195-201
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• 2017

Urbanization and industrialization have led to a severe deterioration in urban air qualities. As an air pollution is closely associated with human health, citizen has a growing concern about the air quality policy to improve their residential environment. Due to the recently advance in web- and mobile computing technologies, citizens can easily access various air quality information and public authorities utilize the social media service to communicate citizens with air quality issues. Also citizens' participation and role has been increased to improve urban air qualities. Thus, to meet the technical and societal changes, a GIS-based air quality information system is developed based upon the Ubiquitous Public Access (UPA) model in ISO19154. The proposed system employs the context information model to provide air quality information depending on citizens' health conditions and locations. Also, citizens can present their perceptions of an air quality at their current location Public authorities can thus utilize the citizens' perceptions when establishing new air quality policies.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.4 no.1
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• pp.11-23
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• 2000

Photochemical air quality models are essential tools in predicting future air quality and assessing air pollution control strategies. To evaluate air quality using a photochemical air quality model, emission inventories are important inputs to these models. Since most emission inventories are provided at a county-level, these emission inventories need to be geographically allocated to the computational grid cells of the model prior to running the model. The conventional method for the spatial allocation of these emissions uses "spatial surrogate indicators", such as population for mobile source emissions and county area for biogenic source emissions. In order to examine the applicability of such approximations, more detailed spatial surrogate indicators were developed using Geographic Information System(GIS) tools to improve the spatial allocation of mobile and boigenic source emissions, The proposed spatial surrogate indicators appear to be more appropriate than conventional spatial surrogate indicators in allocating mobile and biogenic source emissions. However, they did not provide a substantial improvement in predicting ground-level ozone(O3) concentrations. As for the carbon monoxide(CO) concentration predictions, certain differences between the conventional and new spatial allocation methods were found, yet a detailed model performance evaluation was prevented due to a lack of sufficient observed data. The use of the developed spatial surrogate indicators led to higher O3 and CO concentration estimates in the biogenic source emission allocation than in the mobile source emission allocation.llocation.

• Journal of Environmental Health Sciences
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• v.49 no.6
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• pp.295-304
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• 2023

Background: Adolescents are relatively more sensitive than adults to exposure to indoor pollutants. The indoor air quality in classrooms where students spend time together must therefore be managed at a safe level because it can affect the health of students. Objectives: In this study, three types of green-wall models were applied to classrooms where students spend a long time in a limited space, and the resulting effects on reducing PM were evaluated. Methods: In the middle school classrooms which were selected as the experimental subjects, IoT-based indoor air quality monitoring equipment was installed for real-time monitoring. Three types of plant models (passive, active, and active+light) were installed in each classroom to evaluate the effects on improving indoor air quality. Results: The concentration of PM in the classroom is influenced by outdoor air quality, but repeated increases and decreases in concentration were observed due to the influence of students' activities. There was a PM reduction effect by applying the green-wall model. There was a difference in PM reduction efficiency depending on the type of green-wall model, and the reduction efficiency of the active model was higher than the passive model. Conclusions: The active green-wall model can be used as an efficient method of improving indoor air quality. Additionally, more research is needed to increase the efficiency of improving indoor air quality by setting conditions that can stimulate the growth of each type of plant.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.687-696
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• 2018

In order to develop effective emission abatement strategies for coal-fired power plants, we analyzed the shutdown effects of coal-fired power plants on $PM_{2.5}$ concentration in June by employing air quality model for the period from 2013 to 2016. WRF (Weather Research and Forecast) and CMAQ(Community Multiscale Air Quality) models were used to quantify the impact of emission reductions on the averaged $PM_{2.5}$ concentrations in June over Chungcheongnam-do area in Korea. The resultant shutdown effects showed that the averaged $PM_{2.5}$ concentration in June decreased by 1.2% in Chungcheongnam-do area and decreased by 2.3% in the area where the surface air pollution measuring stations were located. As a result of this study, it was confirmed that it is possible to analyze policy effects considering the change of meteorology and emission and it is possible to quantitatively estimate the influence at the maximum impact region by utilizing the air quality model. The results of this study are expected to be useful as a basic data for analyzing the effect of $PM_{2.5}$ concentration change according to future emission changes.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.157-160
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• 2010

Management of indoor air quality of underground subway station is an important issue since the limited natural ventilation, limited sunshine incoming, and highly moistured atmosphere. The improvement in IAQ of platform is expected because most stations were installed with platform screen door currently, however, the poor air quality in tunnel might be affecting subway cabin indoor. In this study, we developed the air quality assessment model based on computational fluid dynamics. The geometry of air ventilation unit, seat, LCD monitors, and passengers were modeled using commercial software (Design Modeler) and fluid pattern and pollutants trajectories were analyzed by using CFX. We predicted the thermal comfort by predicted mean vote (PMV), distribution of CO2 and PM10 concentration. It is expected that this model can be used for the performance test of air cleaners which are under development.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.E
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• pp.45-54
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• 1995

Indoor air quality models are useful to predict indoor air pollutant concentrations as a function of several indoor factors. Indoor air quality model was developed to evaluate the pollutant removal efficiency of variable-air-volume/bypass filtration system (VAV/BPFS) compared with the conventional variable-air-volume (VAV) system. This model provides relative pollutant removal effectiveness of VAV/BPFS by concentration ratio between the conventional VAV system and VAV/BPFS. The predictions agree closely, from 5 to 10 percent, with the measured values for each energy load. As a results, we recommend the VAV/BPFS is a promising alternative to conventional VAV system because it is capable of reducing indoor air pollutant concentration and maintaining good indoor air quality.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.4
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• pp.407-438
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• 2013

Current status and future direction of air quality modeling for monitoring and forecasting air quality in East Asia were discussed in this paper. An integrated air quality modeling system, combining (1) emission processing and modeling, (2) meteorological model simulation, (3) chemistry-transport model (CTM) simulation, (4) ground-based and satellite-retrieved observations, and (5) data assimilation, was introduced. Also, the strategies for future development of the integrated air quality modeling system in East Asia was discussed in this paper. In particular, it was emphasized that the successful use and development of the air quality modeling system should depend on the active applications of the data sets from incumbent and upcoming LEO/GEO (Low Earth Orbit/Geostationary Earth Orbit) satellites. This is particularly true, since Korea government successfully launched Geostationary Ocean Color Imager (GOCI) in June, 2010 and has another plan to launch Geostationary Environmental Monitoring Spectrometer (GEMS) in 2018, in order to monitor the air quality and emissions in/around the Korean peninsula as well as over East Asia.