The Influence of the eyespots of peacock butterfly (Aglais io) and caterpillar on predator recognition

The main purpose of this study is to verify or refute the famous existing theory that the eyespots found on the wings of various insects are a kind of imitation which triggers birds, the predator of insects, to have a sense of avoidance by making them recognize the insects as their predator. The first experiment was conducted on the peacock butterfly using models with eyespots and those without eyespots. A single butterfly model without eyespots was used as the control group, and a pair of a butterfly models with eyespots and another without eyespots was used as the treated group. The butterfly models were attached to trees and the survival rate of the models without eyespots was checked every hour. According to the results of the experiment, it is difficult to conclude that the eyespots of peacock butterfly trigger a sense of avoidance for birds as there was no significant difference in the numbers of the attacked peacock butterfly models without eyespots between the control group and the treated group. The second experiment was conducted using caterpillar models with eyespots and those without eyespots arranged in the same way as the first experiment. However, there was no statistically significant difference in the numbers of attacked caterpillar models between the control group of a caterpillar model without eyespots only and the treated group composed of a pair of caterpillar models without eyespots and the one with eyespots. Thus, the second experiment shows that the caterpillar with eyespots does not imitate the eyes of the predator and it indirectly supports the findings of the first experiment. Through the results of the two experiments, it is possible to refute the existing theory that the eyespots actually imitate the eyes of the natural enemy of the predator

The Assessment of Quality of Comprehensive Plan for Storm and Flood Damage Reduction in Korea

Department of Urban and Environmental Engineering(Disaster Management Engineering)A comprehensive plan for storm and flood damage reduction (CPSFDR), required by Article 16 of Countermeasures against Natural Disasters Act, has a goal to reduce human and property damage and to make safety community. A CPSFDR is dealing with typhoon, flood, waves, tsunami, heavy snow and other natural disasters. The purpose of this study is to develop the plan quality index to assess the quality of CPSFDRs in Korea. Total 75 elements were developed about fact basis, mitigation measures and plan implementation base on literature review, and three coders participated to assess plans. Content analysis was used to assess the quality of plans with 0-to-2 ordinal scale. 49 CPSFDRs were analyzed for this study among 158 municipalities. The result of assessment was compared interregional difference among assessment indices, and it was used to statistical analysis such as t-test and correlation analysis. Through this study, four issues were found. First, CPSFDRs aim to structural mitigation measures mostly. Appropriate mixed using with structural measures and non-structural measures is important for effective disaster mitigation. Second, there is no regional difference between mitigation measures. There are similar mitigation measures in most municipalities, though there are various regional characteristics and ability to cope with natural disaster. Third, connectivity is deficient between CPSFDR and other disaster related plan such as an urban master plan and a river comprehensive plan. Especially, there are problem that overlap with hazard risk area and urban planned area. Fourth, there is no evaluation and monitoring plan in plan implementation section. Continuous evaluation and monitoring should be enforced before renewal, but detailed plans of them are not proposed in plansope

First-time comparison between NO2 vertical columns from GEMS and Pandora measurements

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV&ndash;visible spectrometer onboard the GEO-KOMPSAT-2B satellite launched into geostationary orbit in February 2020. To evaluate GEMS NO2 column data, comparison was carried out using NO2 vertical column density (VCD) measured using direct-sunlight observations by the Pandora spectrometer system at four sites in Seosan, South Korea, during November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO2 data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7 &times; 1015 molec. cm-2 to 5.5 &times; 1015 molec. cm-2 for cloud fraction (CF) &lt; 0.7. Higher correlation coefficients of 0.62&ndash;0.78 with lower RMSEs from 3.3 &times; 1015 molec. cm-2 to 4.3 &times; 1015 molec. cm-2 were found with CF &lt; 0.3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less-cloudy conditions. Overall, GEMS NO2 column data tend to be lower than those of Pandora due to differences in representative spatial coverage, with a large negative bias under high-CF conditions. With correction for horizontal representativeness in Pandora measurement coverage, the correlation coefficients range from 0.69 to 0.81 with RMSEs from 3.2 &times; 1015 molec. cm-2 to 4.9 &times; 1015 molec. cm-2 were achieved for CF &lt; 0.3, showing the better correlation with the correction than that without the correction.</p

First-time comparison between NO2 vertical columns from Geostationary Environmental Monitoring Spectrometer (GEMS) and Pandora measurements

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV-visible (UV-Vis) spectrometer on board the GEO-KOMPSAT-2B (Geostationary Korea Multi-Purpose Satellite 2B) satellite launched into a geostationary orbit in February 2020. To evaluate the GEMS NO2 total column data, a comparison was carried out using the NO2 vertical column density (VCD) that measured direct sunlight using the Pandora spectrometer system at four sites in Seosan, South Korea, from November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO2 data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7×1015 to 5.5×1015 molec. cm−2 for a cloud fraction (CF) &lt;0.7. Higher correlation coefficients of 0.62–0.78 with lower RMSEs from 3.3×1015 to 5.0×1015 molec. cm−2 were found with CF &lt;0.3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less cloudy conditions. Overall, the GEMS NO2 total column data tended to be lower than the Pandora data, owing to differences in the representative spatial coverage, with a large negative bias under high CF conditions. With a correction for horizontal representativeness in the Pandora measurement coverage, correlation coefficients ranging from 0.69 to 0.81, with RMSEs from 3.2×1015 to 4.9×1015 molec. cm−2, were achieved for CF &lt;0.3, showing a better correlation with the correction than without the correction.</p

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA&apos;s TEMPO and ESA&apos;s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Operational Algorithm of Aerosol Effective Height from the Geostationary Environment Monitoring Spectrometer (GEMS)

The Geostationary Environment Monitoring Satellite (GEMS) retrieves several species of trace gases and aerosol properties. For the aerosol property, retrieval results from the GEMS can be used for the surface air quality analysis and aerosol effect for the airmass factor (AMF) calculation. To provide accurate information on aerosol, in addition, aerosol vertical information is also retrieved from the GEMS defined by the aerosol effective height (AEH). The AEH can help to estimate the AMF for tropospheric trace gases and surface concentration of particulate matter (PM). The aerosol vertical distribution is relatively difficult to retrieve compared to those of clouds, because the optical property of aerosol is various due to the various aerosol types in the atmosphere. For the UV-visible hyperspectral observation, the aerosol vertical distribution can estimate from the absorption bands based on the Oxygen molecules, such as O2-A, O2-B, and O2-O2 absorption. Because of the limitation for the spectral coverage from 300~500 nm, however, GEMS is only available to use O2-O2 absorption bands. For the possibility of the AEH retrieval algorithm from GEMS, Park et al. (2016) investigated the theoretical sensitivity test of the AEH retrieval by solely using the O2-O2 absorption band with considering the aerosol and surface properties. Based on the previous studies, we introduce the operational retrieval algorithm for AEH with the theoretical basement. Also, we showed the performance of the operational AEH algorithm from GEMS based on case studies and the validation study using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)