• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.191-211
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• 2017

Assessment of a storm is accomplished by the duration and storm area rather than a simple analysis given by the ground rain gauge stations. One of the best method for assessing storm is Depth-Area-Duration(DAD) of rainfall analysis. but existing DAD analysis method is likely to possible errors. Therefore, DAD analysis and its applicability were examined using a grid-based DAD analysis program that can reduce the possibility of errors in this study. Three spatial distribution techniques were used to analyze the applicability. Then, DAD analysis was performed using the converted grid-based rainfall data. As a result, it was possible to estimate the MAAR values by area for each duration, and showed high applicability in the rainfall data using ordinary kriging technique.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2818-2827
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• 2015

The spatial patterns and characteristics of these sequential earthquakes and ground motions induced by the earthquakes are examined by contours of peak ground velocity (PGV) and geometric mean peak ground velocity (GMPGV) using both ordinary kriging in geographical information system (GIS) and data, the records obtained from strong motion stations, acquired after recent sequential earthquakes in Canterbury, New Zealand (NZ). The performance of underground water pipeline system is examined by using data acquired after earthquakes. The spatial distribution of GMPGV is superimposed on water pipeline repairs throughout the water distribution system in areas affected principally by transient ground motion using GIS and then water pipeline repair rates, expressed as repairs/km, for different types of pipe are evaluated relative to the estimated GMPGV outside liquefaction areas. The earthquake performance of underground water pipeline systems is summarized in this study.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.379-394
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• 2021

Air pollution data in South Korea is provided on a real-time basis by Air Korea stations since 2005. Previous studies have shown the feasibility of gridding air pollution data, but they were confined to a few cities. This paper examines the creation of nationwide gridded maps for PM10 concentration using 333 Air Korea stations with variogram optimization and ordinary kriging. The accuracy of the spatial interpolation was evaluated by various sampling schemes to avoid a too dense or too sparse distribution of the validation points. Using the 114,745 matchups, a four-round blind test was conducted by extracting random validation points for every 365 days in 2019. The overall accuracy was stably high with the MAE of 5.697 ㎍/m³ and the CC of 0.947. Approximately 1,500 cases for high PM10 concentration also showed a result with the MAE of about 12 ㎍/m³ and the CC over 0.87, which means that the proposed method was effective and applicable to various situations. The gridded maps for daily PM10 concentration at the resolution of 0.05° also showed a reasonable spatial distribution, which can be used as an input variable for a gridded prediction of tomorrow's PM10 concentration.

• Journal of Environmental Health Sciences
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• v.50 no.1
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• pp.6-15
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• 2024

Background: People's activities have been restricted due to the COVID-19 pandemic. These changes in activity patterns may lead to a decrease in fine particulate matter (PM_2.5) concentrations. Additionally, the level of population exposure to PM_2.5 may be changed. Objectives: This study aimed to analyze the impact of population movement and meteorological factors on the distribution of PM_2.5 concentrations before and after the outbreak of COVID-19. Methods: The study area was Guro-gu in Seoul. The research period was selected as January to March 2020, a period of significant population movement changes caused by COVID-19. The evaluation of the dynamic population was conducted by calculating the absolute difference in population numbers between consecutive hours and comparing them to determine the daily average. Ambient PM_2.5 concentrations were estimated for each grid using ordinary kriging in Python. For the population exposure assessment, the population-weighted average concentration was calculated by determining the indoor to outdoor population for each grid and applying the indoor to outdoor ratio to the ambient PM_2.5 concentration. To assess the factors influencing changes in the ambient PM_2.5 concentration, a statistical analysis was conducted, incorporating population mobility and meteorological factors. Results: Through statistical analysis, the correlation between ambient PM_2.5 concentration and population movement was positive on both weekends and weekdays (r=0.71, r=0.266). The results confirmed that most of the relationships were positive, suggesting that a decrease in human activity can lead to a decrease in PM_2.5 concentrations. In addition, when population-weighted concentration averages were calculated and the exposure level of the population group was compared before and after the COVID-19 outbreak, the proportion of people exceeding the air quality standard decreased by approximately 15.5%. Conclusions: Human activities can impact ambient concentrations of PM_2.5, potentially altering the levels of PM_2.5 exposure in the population.