• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.4
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• pp.298-304
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• 2021

This study aimed to determine the applicability of drones and air quality sensors in environmental monitoring of air pollutant emissions by developing and testing two new methods. The first method used orthoimagery for precise monitoring of pollutant-emitting facilities. The second method used atmospheric sensors for monitoring air pollutants in emissions. Results showed that ground sample distance could be established within 5 cm during the creation of orthoimagery for monitoring emissions, which allowed for detailed examination of facilities with naked eyes. For air quality monitoring, drones were flown on a fixed course and measured the air quality in point units, thus enabling mapping of air quality through spatial analysis. Sensors that could measure various substances were used during this process. Data on particulate matter were compared with data from the National Air Pollution Measurement Network to determine its future potential to leverage. However, technical development and applications for environmental monitoring of pollution-emitting facilities are still in their early stages. They could be limited by meteorological conditions and sensitivity of the sensor technology. This research is expected to provide guidelines for environmental monitoring of pollutant-emitting facilities using drones.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.63-69
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• 2022

We develop a smart tourism information system that provides smart tourism services to free independent tourists (FITs) through various content distribution channels such as mobile apps, Instagram, YouTube, and chatbots. The smart tourism information system provides location and storytelling-based tourism information, accommodation, restaurant information, and recommended travel products so that tourists can create a travel itinerary based on personalized situation awareness. The smart tourism information system also provides smart tourism services using commercial maps, navigation, and weather forecast APIs from the Korea Meteorological Administration to provide smart tour guide services to tourists who travel according to the travel itinerary. In this paper, we develop the React Native app that provides smart tourism services provided by the smart tourism information system. The smart tourism React Native app has implemented two methods: a method that directly connects to the smart tourism information system, and a method that provides services by interworking through the GraphQL Query Language developed by META (Facebook). The smart tourism React Native app implements OSMU (One Source Multi-use) by providing tourism information from mobile apps, photos from Instagram, and drone videos from YouTube as an integrated UI.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.117-117
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• 2020

The main objective of this study was to setup model and evaluate the model performance for streamflow simulation in Burundi using Soil and Water Assessment Tool (SWAT) model. The total area of Burundi is 27,834 ㎢. The elevation of Burundi ranges from 780 m to 2,700m. The West and East are low lands, while the Central part is high land. The topographic data (30 meters Digital Elevation Model) and land use and land cover data of Burundi were obtained respectively from Shuttle Radar Topography Mission (SRTM) and the Regional Centre for Mapping of Resources for Development (RCMRD). The soil data used was obtained from Food and Agriculture Organization (FAO). The local weather data and discharge data were provided by Burundi Hydro meteorological Service (IGEBU). Mean Areal Precipitation (MAP) and Mean Areal Temperature (MAT) were estimated. The streamflow simulation was done for the period 1980-2017. The calibration and validation of river discharge was performed at a daily time step from 2005 through 2011 as the calibration period and 2012 up to 2017 as the validation period. The findings show that streamflow decreases during Jun to September and increases during March to May and October to December.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of Environmental Impact Assessment
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• v.24 no.2
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• pp.134-153
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• 2015

When making urban planning, it is important to understand climate effect caused by urban structural changes. Seoul city applies UPIS(Urban Plan Information System) which provides information on urban planning scenario. Technology for analyzing climate effect resulted from urban planning needs to developed by linking urban planning scenario provided by UPIS and climate analysis model, CAS(Climate Analysis Seoul). CAS develops for analyzing urban climate conditions to provide realistic information considering local air temperature and wind flows. Quantitative analyses conducted by CAS for the production, transportation, and stagnation of cold air, wind flow and thermal conditions by incorporating GIS analysis on land cover and elevation and meteorological analysis from MetPhoMod(Meteorology and atmospheric Photochemistry Meso-scale model). In order to reflect land cover and elevation of the latest information, CAS used to highly accurate raster data (1m) sourced from LiDAR survey and KOMPSAT-2(KOrea Multi-Purpose SATellite) satellite image(4m). For more realistic representation of land surface characteristic, DSM(Digital Surface Model) and DTM(Digital Terrain Model) data used as an input data for CFD(Computational Fluid Dynamics) model. Eight inflow directions considered to investigate the change of flow pattern, wind speed according to reconstruction and change of thermal environment by connecting green area formation. Also, MetPhoMod in CAS data used to consider realistic weather condition. The result show that wind corridors change due to reconstruction. As a whole surface temperature around target area decreases due to connecting green area formation. CFD model coupled with CAS is possible to evaluate the wind corridor and heat environment before/after reconstruction and connecting green area formation. In This study, analysis of climate impact before and after created the green area, which is part of 'Connecting green network across the north and south in Seoul' plan, one of the '2020 Seoul master plan'.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.123-135
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• 2013

The reflectance observed in the visible channels of a geostationary meteorological satellite can be used to calculate the amount of cloud by comparing the reflectance with the observed solar radiation data at the ground. Using this, the solar radiation arriving at the surface can be estimated. This study used the Meteorological Imager (MI) reflectance observed at a wavelength of 675 nm and the Geostationary Ocean Color Imager (GOCI) reflectance observed at similar wavelengths of 660 and 680 nm. Cloudy days during a typhoon and sunny days with little cloud cover were compared using observation data from the geostationary satellite. Pixels that had more than 40% reflectance in the satellite images showed less than 0.3 of the cloud index and blocked more than 70% of the solar energy. Pixels that showed less than 15% reflectance showed more than 0.9 of the cloud index and let through more than 90% of the solar energy to the surface. The calculated daily accumulated solar radiation was compared with the observed daily accumulated solar radiation in 22 observatories of the Korean Meteorological Administration. The values calculated for the COMS and MTSAT MI sensors were smaller than the observation and showed low correlations of 0.94 and 0.93, respectively, which were smaller than the 0.96 correlation coefficient calculated for the GOCI sensor. The RMSEs of MTSAT, COMS MI and GOCI calculation results showed 2.21, 2.09, 2.02 MJ/$m^2$ in order. Comparison of the calculated daily accumulated results from the GOCI sensor with the observed data on the ground gave correlations and RMSEs for cloudy and sunny days of 0.96 and 0.86, and 1.82 MJ/$m^2$ and 2.27 MJ/$m^2$, respectively, indicating a slightly higher correlation for cloudy days. Compared to the meteorological imager, the geostationary ocean color imager in the COMS satellite has limited observation time and observation is not continuous. However, it has the advantage of providing high resolution so that it too can be useful for solar energy analysis.

• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.514-524
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• 2016

The Bio-Climatic impact Assessment System, BioCAS was utilized to produce analysis maps of daily maximum perceived temperature ($PT_{max}$) and excess mortality ($r_{EM}$) over the entire Seoul area on a heat wave event. The spatial resolution was 25 m and the Aug. 5, 2012 was the selected heat event date. The analyzed results were evaluated by comparing with observed health impact data - mortality and morbidity - during heat waves in 2004-2013 and 2006-2011,respectively. They were aggregated for 25 districts in Seoul. Spatial resolution of the comparison was equalized to district to match the lower data resolution of mortality and morbidity. Spatial maximum, minimum, average, and total of $PT_{max}$ and $r_{EM}$ were generated and correlated to the health impact data of mortality and morbidity. Correlation results show that the spatial averages of $PT_{max}$ and $r_{EM}$ were not able to explain the observed health impact. Instead, spatial minimum and maximum of $PT_{max}$ were correlated with mortality (r=0.53) and morbidity (r=0.42),respectively. Spatial maximum of $PT_{max}$, determined by building density, affected increasing morbidity at daytime by heat-related diseases such as sunstroke, whereas spatial minimum, determined by vegetation, affected decreasing mortality at nighttime by reducing heat stress. On the other hand, spatial maximum of $r_{EM}$ was correlated with morbidity (r=0.52) but not with mortality. It may have been affected by the limit of district-level irregularity such as difference in base-line heat vulnerability due to the age structure of the population. Areal distribution of the heat impact by local building and vegetation, such as spatial maximum and minimum, was more important than spatial mean. Such high resolution analyses are able to produce quantitative results in health impact and can also be used for economic analyses of localized urban development.

• Journal of Climate Change Research
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• v.5 no.1
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• pp.13-19
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• 2014

In the past, Korea agro-climatic zone except Jeju-do was classified into nineteen based on rice culture by using air temperature, precipitation, and sunshine duration etc. during rice growing periods. It has been used for selecting safety zone of rice cultivation and countermeasures to meteorological disasters. In this study, the climatic variables such as air temperature, precipitation, and sunshine duration of twenty agro-climatic zones including Jeju-do were compared decennially (1970's, 1980's, 1990's, and 2000's). The meteorological data were obtained in Meteorological Information Portal Service System-Disaster Prevention, Korea Meteorological Administration. The temperature of 1970s, 1980s, 1990s, and 2000s were $12.0{\pm}0.14^{\circ}C$, $11.9{\pm}0.13^{\circ}C$, $12.2{\pm}0.14^{\circ}C$, and $12.6{\pm}0.13^{\circ}C$, respectively. The precipitation of 1970s, 1980s, 1990s, and 2000s were $1,270.3{\pm}20.05mm$, $1,343.0{\pm}26.01mm$, $1,350.6{\pm}27.13mm$, and $1,416.8{\pm}24.87mm$, respectively. And the sunshine duration of 1970s, 1980s, 1990s, and 2000s were $421.7{\pm}18.37hours$, $2,352.4{\pm}15.01hours$, $2,196.3{\pm}12.32hours$, and $2,146.8{\pm}15.37hours$, respectively. The temperature in Middle-Inland zone ($+1.2^{\circ}C$) and Eastern-Southern zone ($+1.1^{\circ}C$) remarkably increased. The temperature increased most in Taebak highly Cold zone ($+364mm$) and Taebak moderately Cold Zone ($+326mm$). The sunshine duration decreased most in Middle-Inland Zone (-995 hours). The temperature (F=2.708, df=3, p= 0.046) and precipitation (F=5.037, df=3, p=0.002) increased significantly among seasons while the sunshine duration decreased significantly(F=26.181, df=3, p<0.0001) among seasons. In further study, it will need to reclassify agro-climatic zone of rice and it will need to conduct studies on safe cropping season, growth and developing of rice, and cultivation management system etc. based on reclassified agro-climatic zone.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.6
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• pp.35-50
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• 2022

As the water shortage has become a noticeable issue due to climate change, forests play an importance role as the provider of water supply service. There is, however, little information about the relationships between the factors used in the estimation of water supply service and coarse pore fraction of forest soil which determines the potential of water supply. To find out whether there would be an amelioration in the scoring system of water supply service estimation, we examined all factors except meteorological one and additionally, analyzed 4 extra factors that might be related with coarse pore fraction of soil. A total of 2,214 soil samples were collected throughout South Korea to measure coarse pore fractions from 2015 to 2020. First, the result of average coarse pore fraction of all samples showed 32.98±6.59% which was consistent with previous studies. And the results of non-parametric analysis of variance indicated that only two of eleven factors that was used in the scoring system matched the results of coarse pore fraction of forest soils. Tree canopy coverage showed no difference among categories, and slope also showed no significance at level of 0.05 in the linear regression analysis. Additionally, the applicability of 4 extra factors were confirmed, as the result of coarse pore fractions of soil samples were different for various categories of each factor. Therefore, the scoring system of water supply service of forest should be revised to improve accuracy.

• Journal of Environmental Health Sciences
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• v.42 no.2
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• pp.92-101
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• 2016

Objectives: Outdoor workers are exposed to thermally stressful work environments. In this study, heat stress indices for harbor workers in summer were calculated to evaluate thermal comfort based on a human heat balance model. These indices are Physiological Subjective Temperature (PST), Dehydration Risk (DhR), and Overheating Risk (OhR) according to respective stage of cargo work in a harbor. In addition, we constructed a forecast system to provide heat stress information. Methods: Thermophysiological indices in this study were calculated using the MENEX model (i.e. the human heat balance model), which used as inputs the meteorological parameters, clothing insulation, and metabolic rate for each stage of cargo work in the harbor of Masan over the course of seven days, including a four-day heat wave. The forecast heat stress information constructed for Masan harbor was based on meteorological data supported by the Dong-Nae Forecast from the KMA (Korea Metrological Administration) and other input parameters. Results: According to higher metabolic rate, thermophysiological indices showed a critical level. In particular, PST was evaluated as reaching the 'Very hot' or 'Hot' level during all seven days, despite the heat occurring over only four. It is important in a regard to consider the work environment conditions (i.e. labor intensity and clothing in harbor). On a webpage, the forecast thermophysiological indices show as infographics to be easily understand. This webpage is comprised of indices for both current conditions and the forecast, with brief guidance. Conclusion: Thermophysiological indices show the risk level to health during a heat wave period. Heat stress information could help to protect the health of harbor workers. Further, this study could extend the applicability of these indices to a variety of outdoor workers in consideration of work environments.