• Environmental Engineering Research
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• 제23권4호
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• pp.430-441
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• 2018

The adverse health impact of air pollution is becoming more serious. The purpose of this study is twofold: One is to analyze the effect of air pollution and temperatures on human health by analyzing the number of deaths from cardiovascular disease in Seoul, Korea; the other is to determine what impact the location of a monitoring site has on the results of a health study. For this latter purpose, air pollution and temperature monitors are sited at three locations termed green, public, and residential. Then, a decision tree model is used to analyze factors linked with deaths occurring at each monitoring site. The results show that the environmental temperatures before death and the $PM_{2.5}$ concentrations on the day of death are highly linked with the number of deaths regardless of the monitoring location. However, results are most accurate with residential data. The results of this study can be used as base data for a similar analysis and ultimately, as a guide to minimize the health impact of air pollution.

• 환경영향평가
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• 제16권1호
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• pp.79-87
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• 2007

The performance of a stream water quality analysis model depends upon many factors attributed to the geological characteristics of a watershed as well as the distribution behaviors of pollutant itself on a surface of watershed. Because the model run has to import the pollution load from the watershed as a boundary condition along an interface between a stream water body and a watershed, it has been used to introduce a pollution delivery coefficient to behalf of the boundary condition of load importation. Although a nonlinear regression model (NRM) was developed to cope with the limitation of a conventional empirical way, this an up-to-date study has also a limitation that it can't be applied where the pollution load washed off (assumed at a source) is less than that delivered (observed) in a stream. The objective of this study is to identify what causes the limitation of NRM and to suggest how we can purify the process to evaluate a pollution delivery coefficient using many field observed cases. As a major result, it was found what causes the pollution load delivered to becomes bigger than that assumed at the source. In addition, the pollution load discharged to a stream water body from a specific watershed was calculated more accurately.

• 한국대기환경학회지
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• 제26권3호
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• pp.276-285
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• 2010

Recently the Korean government has tried to cut down the $PM_{10}$ concentration by the Special Law for Air Quality Improvement. But the concentrations of $PM_{10}$ have exceeded the air quality standard at most monitoring stations. Primary $PM_{10}$ emitted from various sources and emission data have large uncertainty. The biomass burning is one of the major sources of $PM_{10}$ emission. The biomass burning is composed of wood stove usage, meat cooking and agricultural combustion etc.. Activity data and emission factors for the biomass burning are limited, and it is hard to calculate the air pollution emissions from these sources. In this study, we tried to estimate the air pollution emission from residential wood stove usage. The number of total wood stoves is estimated by the survey of wood stove manufacturer. And air pollution emission factors for the wood stove are investigated using the flue gas measurement by U.S. EPA particulate test method (Method 5G). As the results, the $PM_{10}$ and CO emission factors of wood stove are estimated as 7.7 g/kg-wood and 78.8 g/kg-wood respectively. The annual $PM_{10}$ and CO emissions from wood stove are calculated as 1,200~3,600 ton/year and 12,600~36,400 ton/year in Korea. It is confirmed that wood stove is the one of major sources of biomass burning, and the survey for activity data and the measurement for emission factors are needed for reducing the uncertainty of these emission data.

• 자원ㆍ환경경제연구
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• 제12권3호
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• pp.515-529
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• 2003

본 연구는 자본과 노동의 요소투입물의 증가가 환경오염의 증가를 유발한다는 Byrne (1997)모형과 기술축적도 환경오염을 유발하는 Bovenberg and Smulders (1995)모형을 혼합한 이론적 모형을 구축하고 경제성장률 제약조건식을 도출하여 경제성장과 환경오염관련 변수 간의 관계를 실증분석하였다. 도출된 경제성장률 조건식에서 경제성장률은 상대적인 소비와 오염의 비효용이 감소할수록, 할인율이 감소할수록, 오염저감기술수준이 증가할수록, 기술생산성 파라메타가 증가할수록 증가함을 보였다. OECD 20개국 패널자료를 이용한 실증분석에서 노동생산성이나 총고정자본, 기술이전과 같은 성장관련 주요 변수들은 모두 유의한 것으로 나타나 경제성장 관련 기존의 연구결과와 다르지 않았다. 그러나 경제성장에 대한 환경오염변수의 추정계수가 유의한 값으로 나타나지 않아 이론적으로 도출한 경제성장률조건식의 설명에 한계가 있으나 오히려 환경쿠즈네츠곡선가설의 존재여부를 판단할 수 있는 실증분석 연구과제를 남기는 데 의미가 있다고 본다.

• Journal of Preventive Medicine and Public Health
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• 제57권2호
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• pp.138-147
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• 2024

Objectives: Fine particulate matter pollution has emerged as a significant life-threatening issue in Thailand. Recognizing the importance of environmental health literacy (EHL) in disease prevention is crucial for protecting public health. This study investigated EHL levels and aimed to identify associated factors among village health volunteers (VHVs) in the upper northern region of Thailand. Methods: A cross-sectional study was conducted to collect data from 710 VHVs using the EHL assessment tool developed by the Department of Health, Thailand. Results: The overall EHL score was moderate (mean, 3.28 out of a possible 5.0), with the highest and lowest domain-specific mean score for the ability to make decisions (3.52) and the ability to access (3.03). Multiple linear regression revealed that the factors associated with EHL score were area of residence (urban areas in Chiang Mai: B=0.254; urban areas in Lampang: B=0.274; and rural areas in Lampang: B=0.250 compared to rural areas in Chiang Mai), higher education levels (senior high school: B=0.212; diploma/high vocational certificate: B=0.350; bachelor's degree or above: B=0.528 compared to elementary school or lower), having annual health checkups compared to not having annual health check-ups (B=0.142), monthly family income (B=0.004), and individuals frequently facing air pollution issues around their residence (B=0.199) compared to those who reported no such issues. Conclusions: The VHVs exhibited moderate EHL associated with residence area, education, health check-ups, family income, and residential air pollution. Considering these factors is vital for enhancing VHVs' EHL through strategic interventions.

• 수산경영론집
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• 제41권2호
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• pp.135-151
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• 2010

Conventional studies concerning about economic evaluation of fisheries damages caused by public undertakings have focused on showing the causality between marin environmental variation and fisheries production. But almost all of them have ignored the effect of fisheries damages factors on fisheries price. The study tries to suggest a model how to examine the existence and measurement of the effect of fisheries damage factors on fisheries price using statistical approach, in other words, the estimation of the statistical coincidence between two different population means. The paper tries to give a good application of the model using the case of fisheries damages caused by oil leakage pollution which happened three years ago in the coast of Taean province.

• Asian-Australasian Journal of Animal Sciences
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• 제12권8호
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• pp.1298-1309
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• 1999

The nutritional value of protein varies between feedstuffs. It is possible to feed animals using crystalline amino acids as a sole nitrogen source, but in practice only some limiting amino acids are added to the diet. In order to use feedstuffs efficiently, it is important to determine exact amino acid requirements. Reported values differ widely because the requirements are affected by various factors. In this report, therefore, the factors affecting amino acid requirements are reviewed as follows: 1) availability of dietary amino acids, conversion factors of nitrogen to protein, interaction of amino acids, and strain, sex and age of animals; 2) amino acid requirements for maximum performance and maintenance, usefulness of non-essential amino acids; 3) plasma amino acid concentration as a parameter to determine amino acid requirements; and 4) nitrogen excretion to reduce environmental pollution. These factors should be considered, it is to improve the dietary efficiency, which is to reduce excess nitrogen excretion for environmental pollution.

• Journal of Korea Artificial Intelligence Association
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• 제1권2호
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• pp.15-20
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• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• 자원ㆍ환경경제연구
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• 제15권3호
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• pp.405-423
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• 2006

본 연구는 자본과 노동의 요소투입물 증가가 환경오염의 증가를 유발하고 기술축적자체도 환경오염을 유발한다는 가설모형을 설정하고, 사회계획자모형과 환경오염방지활동이 이루어지지 않는 시장경제모형을 구축하여 이론모형을 도출한다. 도출된 이론모형을 이용하여 환경오염변수와 경제성장률(또는 국민소득 수준) 간에 선형이 아닌 비선형동학(nonlinear dynamics) 관계가 존재하는지를 분석하기 위하여 변수의 부드러운 곡면전환이 이루어지는 평활전이자기회귀모형(Smooth Transition Autoregressive : STAR)을 사용하였다. 서울시 산업생산지수와 대기오염도를 이용한 실증분석에서 경제성장률과 환경오염변수 간에 비선형 동태적인 관계와 비선형 그랜저 인과관계가 존재하는 것으로 나타나 서울지역에서의 환경쿠즈네츠곡선가설이 성립하고 있음을 간접적으로 확인하였다. 그러나 그 해석에는 한계가 있음을 지적하였다.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.222-232
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• 2021

The power output of solar panels highly depends on environmental situations like weather and air pollution. Due to bad weather or air pollution, it is difficult for solar panels to operate at their full potential. Knowing the power output of solar panels in advance helps set up the solar panels correctly and work their possible potential. This paper presents an approach to predict the power output of solar panels based on weather and air pollution features using machine learning methods. We create machine learning models with three kinds set of features, such as weather, air pollution, and weather and air pollution. Our datasets are collected from the area of Seoul, South Korea, between 2017 and 2019. The experimental results show that the weather and air pollution features can be efficient factors to predict the power output of solar panels.