To investigate a proper sampling method for anchovy eggs and larvae in coastal waters of the South Sea, replicated samplings were made by different towing methods with different sampling gears and compared in terms of abundance and length composition. There was no significant difference in abundance in samples from vertical and oblique tows with a ring net. The abundance by replicated vertical tows with a ring net was not significantly different, but significant difference in abundance among sampling stations were found. The ring net sampled anchovy eggs in significantly greater numbers than collected by a NORPAC net, but both gears were not effective in obtaining quantitative samples of anchovy larvae larger than 3 mm. Therefore, samples by vertical tows with a ring net during the day at various stations is more efficient at estimating the density of anchovy eggs in an area compared to replicated sampling at a single station.
Five years have passed since the first set of environmental samples was taken in 2011 to represent various ecosystems which would help future generations lead back to the past environment. Those samples have been preserved cryogenically in the National Environmental Specimen Bank(NESB) at the National Institute of Environmental Research. Even though there is a strict regulation (SOP, standard operating procedure) that rules over the whole sampling procedure to ensure each sample to represent the sampling area, it has not been put to the test for the validation. The question needs to be answered to clear any doubts on the representativeness and the quality of the samples. In order to address the question and ensure the sampling practice set in the SOP, many steps to the measurement of the sample, that is, from sampling in the field and the chemical analysis in the lab are broken down to evaluate the uncertainty at each level. Of the 8 species currently taken for the cryogenic preservation in the NESB, pine tree samples from two different sites were selected for this study. Duplicate samples were taken from each site according to the sampling protocol followed by the duplicate analyses which were carried out for each discrete sample. The uncertainties were evaluated by Robust ANOVA; two levels of uncertainty, one is the uncertainty from the sampling practice, and the other from the analytical process, were then compiled to give the measurement uncertainty on a measured concentration of the measurand. As a result, it was confirmed that it is the sampling practice not the analytical process that accounts for the most of the measurement uncertainty. Based on the top-down approach for the measurement uncertainty, the efficient way to ensure the representativeness of the sample was to increase the quantity of each discrete sample for the making of a composite sample, than to increase the number of the discrete samples across the site. Furthermore, the cost-effective approach to enhance the confidence level on the measurement can be expected from the efforts to lower the sampling uncertainty, not the analytical uncertainty. To test the representativeness of a composite sample of a sampling area, the variance within the site should be less than the difference from duplicate sampling. For that, a criterion, ${i.e.s^2}_{geochem}$(across the site variance) <${s^2}_{samp}$(variance at the sampling location) was proposed. In light of the criterion, the two representative samples for the two study areas passed the requirement. In contrast, whenever the variance of among the sampling locations (i.e. across the site) is larger than the sampling variance, more sampling increments need to be added within the sampling area until the requirement for the representativeness is achieved.
This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.
Water quality sampling surveys and continuous measurement of flow were conducted to identify wastewater flow characteristics for representative catchment of O and M treatment basins in A city. For HS-1 station representing commercial area, wastewater flow rises in the beginning of office-working hours, moves up and down within narrow range, and lasts till office-leaving hour, and falls gradually reflecting worker's returning home. However, in HS-2 station representing residential area, wastewater flow has two peaks, which are before office-going hour and after office-leaving hour. In residential area, the flow rate of weekends is higher than that of weekdays because it reflects population, being not contributed to generate wastewater during the working hours of weekdays, stay home and produce wastewater for weekends period. To determine the priority for rehabilitation of sewer system, infiltration rate was computed by dividing infiltration flow by mean diameter and total length of sewer, and HS-1 station ranked the first.
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
/
v.16
no.3
/
pp.147-154
/
2011
Decision making in Environmental Impact Assessment (EIA) and Consultation on the Coastal Area Utilization (CCAU) is footing on the survey reports, thus requires concrete and accurate information on the natural habitats. In spite of the importance of reporting the ecological quality and status of habitats, the accumulated knowledge and recent techniques in ecology such as the use of investigated cases and indicators/indices have not been utilized in evaluation processes. Even the EIA report does not contain sufficient information required in a decision making process for conservation and development. In addition, for CCAU, sampling efforts were so limited that only two or a few stations were set in most study cases. This hampers transferring key ecological information to both specialist review and decision making processes. Hence, setting the effective number of sampling stations can be said as a prior step for better assessment. We introduced a few statistical techniques to determine the number of sampling stations in macrobenthos surveys. However, the application of the techniques requires a preliminary study that cannot be performed under the current assessment frame. An analysis of the spatial configuration of sampling stations from 19 previous studies was carried out as an alternative approach, based on the assumption that those configurations reported in scientific journal contribute to successful understanding of the ecological phenomena. The distance between stations and number of sampling stations in a $4{\times}4$ km unit area were calculated, and the medians of each parameter were 2.3 km, and 3, respectively. For each study, approximated survey area (ASA, $km^2$) was obtained by using the number of sampling stations in a unit area (NSSU) and total number of sampling stations (TNSS). To predict either appropriate ASA or NSSU/TNSS, we found and suggested statistically significant functional relationship among ASA, survey purpose and NSSU. This empirical approach will contribute to increasing sampling effort in a field survey and communicating with reasonable data and information in EIA and CCAU.
This study describes (1) the impact of positive sampling artifacts caused by not only a filter-based sampling, but also a denuder-based sampling in the determination of particle-phase organic carbon (POC), (2) the effect of sample flow rate on positive artifacts, and (3) an optimum flow rate that provides a minimized negative sampling artifact for the denuder-based sampling method. To achieve the goals of this study, four different sampling media combinations were employed: (1) Quartz filter-alone (Q-alone), (2) quartz filter behind quartz-fiber filter (QBQ), (3) quartz filter and quartz filter behind Teflon filter (Q-QBT), and (4) quartz filter behind carbon-based denuder (Denuder-Q). The measurement of ambient POC was carried out in an urban area. In addition, to determine gas-phase OC (GOC) removal efficiency of the denuder, a Teflon filter and a quartz filter were deployed upstream and downstream of the denuder, respectively with varying sample flow rates: 5, 10, 20, and 30 LPM. It was found that Q-alone sampling configuration showed a higher POC than QBQ, Q-QBT, and Denuder-Q by 12%, 28%, and 23%, respectively at a sample flow rate of 20 LPM due to no correction for positive artifact caused by adsorption of GOC onto the filter. A lower quantity of GOC was collected from the backup quartz filter on QBQ than that from Q-QBT. This was because GOC was not in equilibrium with that adsorbed on the front quartz filter of QBQ during the sampling period. It is observed that the loss of particle number and mass across the denuder increases with decreasing sample flow rate. The contribution o f positive arti facts to POC decreased with increasing sample flow rate, showing 29%, 25%, and 22% for 10, 20, and 30 LPM, respectively. The 20 LPM turns out to be the optimum sample flow rate for both filter and denuder-based POC sampling.
Objectives : An appropriate sampling strategy for estimating an epidemiologic volume of diabetes has been evaluated through a simulation. Methods : We analyzed about 250 million medical insurance claims data submitted to the Health Insurance Review & Assessment Service with diabetes as principal or subsequent diagnoses, more than or equal to once per year, in 2003. The database was re-constructed to a 'patient-hospital profile' that had 3,676,164 cases, and then to a 'patient profile' that consisted of 2,412,082 observations. The patient profile data was then used to test the validity of a proposed sampling frame and methods of sampling to develop diabetic-related epidemiologic indices. Results : Simulation study showed that a use of a stratified two-stage cluster sampling design with a total sample size of 4,000 will provide an estimate of 57.04%(95% prediction range, 49.83 - 64.24%) for a treatment prescription rate of diabetes. The proposed sampling design consists, at first, stratifying the area of the nation into "metropolitan/city/county" and the types of hospital into "tertiary/secondary/primary/clinic" with a proportion of 5:10:10:75. Hospitals were then randomly selected within the strata as a primary sampling unit, followed by a random selection of patients within the hospitals as a secondly sampling unit. The difference between the estimate and the parameter value was projected to be less than 0.3%. Conclusions : The sampling scheme proposed will be applied to a subsequent nationwide field survey not only for estimating the epidemiologic volume of diabetes but also for assessing the present status of nationwide diabetes control.
Park, Eunbeen;Song, Cholho;Ham, Boyoung;Kim, Jiwon;Lee, Jongyeol;Choi, Sol-E;Lee, Woo-Kyun
Journal of Climate Change Research
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v.9
no.4
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pp.385-398
/
2018
Although the importance of developing reliable and systematic GHG inventory has increased, the GIS/RS-based national scale LULUCF (Land Use, Land-Use Change and Forestry) sector analysis is insufficient in the context of the Paris Agreement. In this study, the change in $CO_2$ storage of forest land due to land use change is estimated using two GIS/RS methodologies, Sampling and Wall-to-Wall methods, from 2000 to 2010. Particularly, various imagery with sampling data and land cover maps are used for Sampling and Wall-to-Wall methods, respectively. This land use matrix of these methodologies and the national cadastral statistics are classified by six land-use categories (Forest land, Cropland, Grassland, Wetlands, Settlements, and Other land). The difference of area between the result of Sampling methods and the cadastral statistics decreases as the sample plot distance decreases. However, the difference is not significant under a 2 km sample plot. In the 2000s, the Wall-to-Wall method showed similar results to sampling under a 2 km distance except for the Settlement category. With the Wall-to-Wall method, $CO_2$ storage is higher than that of the Sampling method. Accordingly, the Wall-to-Wall method would be more advantageous than the Sampling method in the presence of sufficient spatial data for GHG inventory assessment. These results can contribute to establish an annual report system of national greenhouse gas inventory in the LULUCF sector.
In order to make clear the relationship between sample design and sample survey in community, it was conducted research on sample design for National Nutrition Survey in 1983. In this paper it was tried to analize the data based on The Report of a Settled Population, 1981 conducted by National Bureau of Statistics Economic Planning Board. The sample was basically using stratified two-stage sampling with systematic sampling of Ban or Li as administrative unit. The population represents the whole nation excluding Jeju-do because of budget. The selection of sampling unit and sampling procedure was as follows. 1) Stratify the nation-wide area in 20 sections according to administrative districts. 2) Determine the sample size in each section according to equal proportional rate (1 / 8040) and to about 1,000 households in the sample. 3) Select the 25 sampling units by section according to households proportion. 4) Select the 10 households at random from each Ban or Li according to equal probability proportion as the final sampling unit. Using the procedure, it was sampled 1,000 households for National Nutrition Survey in 1983.
Journal of the Earthquake Engineering Society of Korea
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v.22
no.2
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pp.63-75
/
2018
This paper proposes extension of Latin Hypercube Sampling (LHS) to avoid the necessity of using intervals with the same probability area where intervals with different probability areas are used. This method is called Weighted Latin Hypercube Sampling (WLHS). This paper describes equations and detail procedure necessary to apply weight function to WLHS. WLHS is verified through numerical examples by comparing the estimated distribution parameters with those from other methods such as Random Sampling and Latin Hypercube Sampling. WLHS provides more flexible way on selecting samples than LHS. Accuracy of WLHS estimation on distribution parameters is depending on the selection of weight function. The proposed WLHS is applied to seismically isolated structures in nuclear power plants. In this application, clearance-to-stops (CSs) calculated using LHS proposed by Huang et al. [1] and WLHS proposed in this paper, respectively, are compared to investigate the effect of choosing different sampling techniques.
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