• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.268-276
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• 2018

BACKGROUND: In the month of January 2018, fine dust alerts and warnings were issued 36 times for $PM_{10}$ and 81 times for PM2.5. Air quality is becoming a serious issue nation-wide. Although interest in air-purifying plants is growing due to the controversy over the risk of chemical substances of regular air-purifying solutions, industrial spread of the plants has been limited due to their efficiency in air-conditioning perspective. METHODS AND RESULTS: This study aims to propose a vegetation-based bio-filter system that can assure total indoor air volume for the efficient application of air-purifying plants. In order to evaluate the quantitative performance of the system, time-series analysis was conducted on air-conditioning performance, indoor air quality, and comfort index improvement effects in a lecture room-style laboratory with 16 persons present in the room. The system provided 4.24 ACH ventilation rate and reduced indoor temperature by $1.6^{\circ}C$ and black bulb temperature by $1.0^{\circ}C$. Relative humidity increased by 24.4% and deteriorated comfort index. However, this seemed to be offset by turbulent flow created from the operation of air blowers. While $PM_{10}$ was reduced by 39.5% to $22.11{\mu}g/m^3$, $CO_2$ increased up to 1,329ppm. It is interpreted that released $CO_2$ could not be processed because light compensation point was not reached. As for the indoor comfort index, PMV was reduced by 83.6 % and PPD was reduced by 47.0% on average, indicating that indoor space in a comfort range could be created by operating vegetation-based bio-filters. CONCLUSION: The study confirmed that the vegetation-based bio-filter system is effective in lowering indoor temperature and $PM_{10}$ and has positive effects on creating comfortable indoor space in terms of PMV and PPD.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.363-369
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• 2018

One of the recent environmental problems is climate change due to the increase of atmospheric $CO_2$, which causes ecological changes and various environmental problems. Therefore, various studies are being carried out to reduce $CO_2$ in the world in order to solve various environmental problems caused by increase of $CO_2$. The $CO_2$ reduction using microalgae is an environmentally friendly method by using photosynthesis reaction of microalgae. However, most studies using single species. There is no study on the $CO_2$ fixing efficiency of microalgae in natural rivers. Therefore, this study was to identify the microalgae in the Sum river and to analyze the growth characteristics of microalgae in the river to obtain optimal culture conditions. And the changes of biomass and chlorophyll-a of microalgae were analyzed according to $CO_2$ concentration and injection rate. The purpose of this study was to investigate the fixing efficiency of carbon dioxide in microalgae in natural rivers. Six kinds of dominant species were observed as a result of the identification of microalgae in Sum river(Ankistrodesmus falcatus, Scenedesmus intermedius, Selenodictyum sp., Xanthidium apiculatum var. laeve, Cosmarium pseudoquinarium, Dictyosphaerium pulchellum). All of these species were green algae. Biomass and chlorophyll-a increased with the increase of $CO_2$ concentration and biomass and chlorophyll-a increased faster flow rate at the same $CO_2$ concentration. Also, the quantity of $CO_2$ fixation on the microalgae tended to be higher when the flow rate of injected gas was faster. This study can be referred as being significant in the micro-algae in river. In addition, the optimal conditions for $CO_2$ fixation of microalgae in rivers and the quantification of the quantity of $CO_2$ fixation from microalgae in rivers can be used as basic data for future policy of $CO_2$ reduction.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.10-20
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• 2019

Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.277-283
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• 2018

The crop growth model has been widely used for climate change impact assessment. Crop growth model require genetic coefficients for simulating growth and yield. In order to determine the genetic coefficients, regional growth monitoring data or yield trial data of crops has been used to calibrate crop growth model. The aim of this study is to verify that yield trial data of corn is appropriate to calibrate genetic coefficients of CERES-MAIZE. Field experiment sites were Suwon, Jinju, Daegu and Changwon. The distance from the weather station to the experimental field were from 1.3km to 27km. Genetic coefficients calibrated by yield trial data showed good performance in silking day. The genetic coefficients associated with silking are determined only by temperature. In CERES-MAIZE model, precipitation or irrigation does not have a significant effect on phenology related genetic coefficients. Although the effective distance of the temperature could vary depending on the terrain, reliable genetic coefficients were obtained in this study even when a weather observation site was within a maximum of 27 km. Therefore, it is possible to estimate the genetic coefficients by yield trial data in study area. However, the yield-related genetic coefficients did not show good results. These results were caused by simulating the water stress without accurate information on irrigation or rainfall. The yield trial reports have not had accurate information on irrigation timing and volume. In order to obtain significant precipitation data, the distance between experimental field and weather station should be closer to that of the temperature measurement. However, the experimental fields in this study was not close enough to the weather station. Therefore, When determining the genetic coefficients of regional corn yield trial data, it may be appropriate to calibrate only genetic coefficients related to phenology.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.1
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• pp.24-30
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• 2019

This study was conducted to investigate the effect of seeding dates on grassland productivity and botanical composition in oversown pasture located in Cheonan of South Korea. Four treatment groups were established based on the seeding dates: 18th August, 1st September, 15th September and 29th September. Evaluation of seasonal changes in botanical composition of pasture showed that the highest ratios of grass in 18th August and 1st September (pasture species 93% and weeds 7%) and the lowest in 15th September (pasture species 75% and weeds 25%). In the plant length, there is no significantly different in 5% probability level. In the total dry matter yield of grass, 18th August ($13,362kg\;ha^{-1}$) and 1st September ($13,988kg\;ha^{-1}$) were higher than 15th September ($11,883kg\;ha^{-1}$) and 29th September ($11,459kg\;ha^{-1}$). The findings of the this study suggest that seeding by early September the most desirable results for botanical composition and grassland productivity in oversown pasture, Cheonan of South Korea.

• Korean journal of applied entomology
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• v.57 no.4
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• pp.261-269
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• 2018

The cowpea aphid Aphis craccivora Koch (Hemiptera: Aphididae) is a polyphagous species with a worldwide distribution. We investigated the temperature effects on development periods of nymphs, and the longevity and fecundity of apterous female of A. craccivora. The study was conducted at six constant temperatures of 10.0, 15.0, 20.0, 25, 30.0, and $32.5^{\circ}C$. A. craccivora developed successfully from nymph to adult stage at all temperatures subjected. The developmental rate of A. craccivora increased as temperature increased. The lower developmental threshold (LT) and thermal constant (K) of A. craccivora nymph stage were estimated by linear regression as $5.3^{\circ}C$ and 128.4 degree-days (DD), respectively. Lower and higher threshold temperatures (TL, TH and TH-TL, respectively) were calculated by the Sharpe_Schoolfield_Ikemoto (SSI) model as $17.0^{\circ}C$, $34.6^{\circ}C$ and $17.5^{\circ}C$. Developmental completion of nymph stages was described using a three-parameter Weibull function. Life table parameters were estimated. The intrinsic rate of increase was highest at $25^{\circ}C$, while the net reproductive rate was highest at $20^{\circ}C$. Biological characteristics of A. craccivora populations from different geographic areas were discussed.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.869-877
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• 2018

The area of desert in East Asia is increasing every year, and it cause a great cost of social damage. Because desert is widely distributed and it is difficult to approach people, remote sensing using satellites is commonly used. But the study of desert area comparison is insufficient which is calculated by satellite sensor. It is important to recognize the characteristics of the desert area data that are calculated for each sensor because the desert area calculated according to the selection of the sensor may be different and may affect the climate prediction and desertification prevention measures. In this study, the desert area of Northeast Asia in 2001-2013 was calculated and compared using Moderate Resolution Imaging Spectroradiometer (MODIS) and Vegetation. As a result of the comparison, the desert area of Vegetation increased by $3,020km^2/year$, while in the case of MODIS, it decreased by $20,911km^2/year$. We performed indirect validation because It is difficult to obtain actual data. We analyzed the correlation with the occurrence frequency of Asian dust affected by desert area change. As a result, MODIS showed a relatively low correlation with R = 0.2071 and Vegetation had a relatively high correlation with R = 0.4837. It is considered that Vegetation performed more accurate desert area calculation in Northeast Asian desert area.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.135-145
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• 2019

Analysis of a long cycle or a trend of time series data based on a long-term observation would require comparability between data observed in the past and the present. In the present study, we proposed an approach to ensure the compatibility among the instruments used for the long-term observation, which would allow to secure continuity of the data. An open-path gas analyzer (Model LI-7500, LI-COR, Inc., USA) has been used for eddy covariance flux measurement in the Gwangneung deciduous forest for more than 10 years. The open-path gas analyzer was replaced by an enclosed-path gas analyzer (Model EC155, Campbell Scientific, Inc., USA) in July 2015. Before completely replacing the gas analyzer, the carbon dioxide ($CO_2$) and latent heat fluxes were collected using both gas analyzers simultaneously during a five-month period from August to December in 2015. It was found that the $CO_2$ fluxes were not significantly different between the gas analyzers under the condition that the daily mean temperature was higher than $0^{\circ}C$. However, the $CO_2$ flux measured by the open-path gas analyzer was negatively biased (from positive sign, i.e., carbon source, to 0 or negative sign, i.e., carbon neutral or sink) due to the instrument surface heating under the condition that the daily mean temperature was lower than $0^{\circ}C$. Despite applying the frequency response correction associated with tube attenuation of water vapor, the latent heat flux measured by the enclosed-path gas analyzer was on average 9% smaller than that measured by the open-path gas analyzer, which resulted in >20% difference of the sums over the study period. These results indicated that application of the additional air density correction would be needed due to the instrument heat and analysis of the long-term observational flux data would be facilitated by understanding the underestimation tendency of latent heat flux measurements by an enclosed-path gas analyzer.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.55-64
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• 2019

The flowering seasons can be advanced due to climate change that would cause an abnormally warm winter. Such warm winter would increase the frequency of crop damages resulted from sudden occurrences of low temperature before and after the vegetative growth stages, e.g., the period from germination to flowering. The degree and pattern of freezing damage would differ by the development stage of each individual fruit tree even in an orchard. A critical temperature, e.g., killing temperature, has been used to predict freeze damage by low-temperature conditions under the assumption that such damage would be associated with the development stage of a fruit flower bud. However, it would be challenging to apply the critical temperature to a region where spatial variation in temperature would be considerably high. In the present study, a phenological model was used to estimate major bud development stages, which would be useful for prediction of regional risks for the freeze damages. We also derived a linear function to calculate a probabilistic freeze risk in spring, which can quantitatively evaluate the risk level based solely on forecasted weather data. We calculated the dates of freeze damage occurrences and spatial risk distribution according to main production areas by applying the spring freeze risk function to apple, peach, and pear crops in 2018. It was predicted that the most extensive low-temperature associated freeze damage could have occurred on April 8. It was also found that the risk function was useful to identify the main production areas where the greatest damage to a given crop could occur. These results suggest that the freezing damage associated with the occurrence of low-temperature events could decrease providing early warning for growers to respond abnormal weather conditions for their farm.