• 한국대기환경학회지
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• 제22권4호
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• pp.518-528
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• 2006

The air quality index (AQI) is an index for reporting daily or hourly air quality to the general public. The AQI focuses on health effects that can happen within a few hours or days after breathing polluted air. Many countries have their own AQI reporting systems, and the HAEI (hourly air environment index) method is now being used in Korea. In this study, in order to compare the AQI results from different methods, we applied three methods. i.e. US AQI, Canadian AQI, and Korean HAEI, to the same air quality data-base. The data-base was constructed from 10 monitoring sites in Gyeong-buk province for the last four years since 2000. Based on the results, a critical evaluation of the Korean HAEI method was made, and a number of suggestions and recommendations were presented to improve the AQI reporting system in Korea.

• Communications for Statistical Applications and Methods
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• 제30권2호
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• 한국조경학회지
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• 제49권5호
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• pp.1-11
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• 2021

본 연구는 도시의 표면을 구성하는 물리적 공간요소 가운데 대기질에 영향을 미치는 주요 요인을 찾아 환경개선을 위한 단서를 제공하는 것이 목적이다. 중국의 산업도시인 창춘의 9개 측정소에서 2018년 1월 1일부터 2019년 12월 31일까지의 AQI 농도 자료를 수집하였다. 측정소를 중심으로 반경 300m 내의 지역을 구성하는 도시의 물리적 시설에 대한 유형과 분포 특징을 분석하였다. 측정소를 3개 그룹으로 나눠 계절별로 미세먼지 농도 차이를 분석한 결과, 봄과 겨울에 AQI 농도가 가장 높고, 다음으로 여름, 가장 낮은 계절은 가을이었다. 봄의 AQI 농도가 가장 높은 곳은 F(93.00)·D(91.10)·I(89.20), 여름 농도가 가장 높은 곳은 D(69.05)·A(67.89)·B(84.44), 가을 농도가 가장 높은 곳은 I(62.80)·G(60.84)·D(53.27), 겨울은 I(95.82)·H(95.60)·F(94.04)이었다. SPSS를 이용한 계열분석을 통해 직경 600m인 공간내의 대기지수는 임야, 초지, 나지, 수공간, 수고, 건축면적(평균치), 건물 체적(평균치)과 상관성이 있는 것으로 나타났다. 오염이 심한 봄과 겨울의 수치자료를 통계분석한 결과, 임야면적(43,637m², 15.44%)과 수면적(18,736m², 6.63%)이 큰 비율을 차지하고, 건축평균면적(448m², 0.17%)과 건축평균부피(10,201m³)가 가장 적은 그룹 1(A, B, C)구역의 오염 농도가 가장 낮았다. 반대로 그룹 2(D, E, F)구역은 AQI 농도가 가장 높은 구역으로 임야(1,917m², 0.68%)와 수면적(0m², 0%)이 적거나 없고 건축평균면적(1,056m², 0.37%)과 건축평균부피(17,470m³)가 가장 높았다. AQI 농도가 가장 높은 지역의 특징은 가로수의 수고가 12m 이상인 경우가 다수 확인되었고, 나지면적의 비율이 적을수록 오염도가 낮은 것으로 나타났다. 동일한 방법으로 창춘의 9개 공간의 특징을 분석한 결과, 도시 공간의 물리적 특성에 따른 대기질은 위의 요인들과 밀접한 관련이 있음을 알 수 있었다.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• 한국정보통신학회논문지
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• 제24권1호
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• pp.8-14
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• 2020

미세먼지의 인체 영향이 밝혀지며 예보정확도 개선에 대한 요구가 증가하고 있다. 이에 기계 학습 기법을 도입하여 예측 정확성을 높이려는 노력이 수행되고 있으나, 저농도 발생 비율이 매우 큰 미세먼지 데이터로 인해 전체 예측 성능이 떨어지는 문제가 있다. 본 논문에서는 PM10 미세먼지 예보 정확도 향상을 위해 농도별 분리 예측 모델을 제안한다. 이를 위해 천안 지역의 기상 및 대기오염 인자를 활용하여 저, 고농도별 예측 모델을 설계하고 전 영역 예측 모델과의 성능 비교를 수행하였다. RMSE, MAPE, 상관계수 및 AQI 정확도를 통한 성능 비교 결과, 전체 기준에서 예측 성능이 향상됨을 확인하였으며, AQI 고농도 예측 성능의 경우 20.62%의 성능 향상이 나타났음을 확인하였다.

• Asian Journal of Business Environment
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• 제10권4호
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• pp.15-19
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• 2020

Purpose: This research aims to explore the level of air pollution in Jakarta, the epicenter of COVID-19 Pandemic in Indonesia and its surrounding provinces during the first month of the Pandemic. Research design, data and methodology: This study uses data, which have been obtained real time from API (Application Programming Interfaces) of air quality website. The measurements of Air Quality Index (AQI), temperature, humidity, and other factors from several cities and regencies in Indonesia were obtained eight times a day. The data collected have been analyzed using descriptive statistics and mapped using QGIS. Results: The finding of this study indicates that The Greater Jakarta Area experienced a decrease in pollutant levels, especially in the Bogor area. Nevertheless, some areas, such as the north Jakarta, have exhibited slow reduction. Furthermore, the regions with high COVID-19 confirmed cases have experienced a decline in AQI. Conclusions: The study concludes that the air quality of three provinces, Jakarta, Banten, and West Java, especially in cities located in the Jakarta Metropolitan Area during COVID-19 pandemic and large-scale social restrictions, is getting better. However, in some regions, the reduction of pollutant concentrations requires a longer time, as it was very high before the pandemic.

• 한국항행학회논문지
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• 제28권1호
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• pp.149-154
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• 2024

미세먼지에 대한 연구는 실시간으로 발전하고 있으며, 예측 모델의 정확도를 향상시키기 위해 다양한 방법이 연구되고 있다. 또한 미세먼지의 정확한 원인과 영향을 파악하기 위해 이러한 다양한 요소들을 고려하는 연구들이 활발히 이루어지고 있다. 본 논문에서는 PM_2.5와 상관성이 있는 데이터를 계절을 기준으로 구분하여 학습하는 예측 모델과 특정 농도를 기준으로 저농도와 고농도를 구분하여 학습하는 모델을 통해 예측 성능의 비교 및 분석을 진행하였다. 기상데이터와 대기오염 물질 데이터를 사용하였으며 PM_2.5와 상관관계를 확인하여 학습 및 평가를 위한 데이터를 구성하였다. 계절별 예측 모델과 농도별 예측 모델은 LSTM으로 설계하였으며, 세부 파라미터는 하이퍼 파라미터 탐색을 통해 적용하였다. 예측 모델의 성능 평가는 정확도, RMSE, MAPE, 저농도와 고농도 구간에서의 정확도 그리고 AQI를 기준으로 4개의 범위에 대한 정확도로 진행하였다. 성능 평가 결과, 농도별 학습을 진행한 예측 모델이 AQI 기준 "나쁨" 구간의 정확도에서 91.02%의 정확도를 보였으며, 계절별 학습을 진행한 예측 모델보다 전반적으로 좋은 성능을 보였다.

• 한국경제지리학회지
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• 제14권2호
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• pp.143-156
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• 2011

최근 심각한 사회문제가 되고 있는 도시환경 문제 해결을 위해, 이를 효과적으로 관리하는 방안이 활발히 연구되고 있다. 환경 지표는 환경을 정량적인 지표로 측정하는 수단으로서, 환경 정책 및 의사결정의 근거로 활용하고 있다. 이 중 대기질 지표는 대기오염 지수를 통해 공기가 얼마나 오염되어 있는지를 표준화하는 지수이다. 우리나라는 미국의 AQI를 국내 대기환경 기준으로 변환한 AEI를 개발하여 대기오염의 정도를 측정한다. 이에 본 연구는 우리나라의 대표 도시인 서울을 연구 지역으로 선정하고, 대기오염 지수와 도시공간구조 특성인 행정 동별 토지이용 및 교통 자료간의 상관성 분석을 시행하였다. 이를 위해 서울시 대기오염 자동 측정망을 통해 측정된 2007년 대기오염 측정 자료를 바탕으로 공간내삽법인 IDW에 의해 전체적인 경향면을 생성하고, 회귀분석, GWR 그리고 대기오염 물질별 시계열적 농도 변화를 분석하였다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권13호
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• pp.5299-5303
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• 2014

Background: To explore etiology for providing scientific clues for the prevention of lung cancer. Materials and Methods: Data for lung cancer incidence and meteorological geographic factors from 25 counties in Zhejiang province of China during 2011 were studied. Stepwise multiple regression and correlation analysis were performed to analyze the geographic distribution and epidemiology of lung cancer. Results: 8,291 new cases (5,998 in males and 2,293 females) of lung cancer during 2011 in Zhejiang province were reported in the 25 studied counties. Reported and standardized incidence rates for lung cancer were 58.0 and 47.0 per 100,000 population, respectively. The incidence of lung cancer increased with age. Geographic distribution analysis shows that the standardized incidence rates of lung cancer in northeastern Zhejiang province were higher than in the southwestern part, such as in Nanhu, Fuyang, Wuxing and Yuyao counties, where the rates were more than 50 per 100,000 population. In the southwestern Zhejiang province, for instance, in Yueqing, Xianju and Jiande counties, the standardized incidence rates of lung cancer were lower than 37 per 100,000 population. Spearman correlation tests showed that forest coverage rate, air quality index (AQI), and annual precipitation level are associated with the incidence of lung cancer. Conclusions: Lung cancer in Zhejiang province shows obvious regional differences. High incidence appears associated with low forest coverage rate, poor air quality and low annual precipitation. Therefore, increasing the forest coverage rate and controlling air pollution may play an important role in lung cancer prevention.

• 대한간호학회지
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• 제30권2호
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• pp.437-451
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• 2000

The purpose of this study was to estimate the forecast of bed demand for institutional long-term care for the elderly persons in Taegu Metropolitan City. The study subject was the total 1,877 elderly persons over age 65 living in Taegu. Among them 1,441 elderly persons were sampled from community and 436 were from the elderly admitted 5 general hospitals. Data collection was carried out by interview from 25 August to 25 December 1997. The measuring instrument of this study was the modified tool of CARE, MAI, PCTC, and ADL which were examined for validity and reliability. In order to forecast bed demand of Nursing Home, this study revised prediction techniques suggested by Robin. The results were as follows : 1. OLDi of Taegu City were 122,202 by the year 1998 and number of Low-Income Elderly Persons were 3,210. 2. The Level I : Senior Citizen Home $ADEMi=\frac{AQi * ASTAYi}{365 * AOCUi}$. AQi = OLDi * LADLi * NASi * ALONi * LIADLi * AUTILi. Predicted number of bed demand for Home Based. Elderly Persons were 4,210 and Low-Income Elderly Persons were 1,081 and Total Elderly Persons were 5,291 by the year 1998, 6,343 by the year 2000 and 8,351 by the 2005. 3. The Level II : Nursing Home $BDEMi=\frac{(BQ1i+BQ2i) * BSTAYi}{365 * BOCUi}$. BQ1i = OLDi * HADLi * ALONi * HIADLi BQ2i = OLDi * HADLi * FAMi * OBEDi Predicted number of demand for Total Elderly Persons were 668 by the year 1998, 802 by the year 2000 and 1,055 by the 2005. 4. The Level III : Nursing Home $CDEMi=\frac{COLDi * HDISi * CUTILi * CSTAYi}{365 * COCUi}+OQi/10$ Predicted number of demand for Total Elderly Persons were 1,899 by the year 1998, 2,311 by the year 2000 and 3,003 by the 2005. 5. Predicted number of bed demand of long-term care facilities in the year 1998 according to Levels were 4.3% among elderly persons in Taegu by Level I, 0.5% by Level II and 1.5% by Level III. Number of elderly persons in current long-term care facilities were 458 in LevelI I,284 in Level II. 6. Deficit number of bed demand of long-term care facilities were 4,833 in Level I, 384 in Level II, 1,899 in Level III for the elderly persons in Taegu Metropolitan City.