• Environmental Analysis Health and Toxicology
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• 제30권
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• pp.10.1-10.14
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• 2015

Recent cohort studies have relied on exposure prediction models to estimate individual-level air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA). For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside sites. This study provided practical knowledge on the computation process of geographic variables in South Korea, which will improve air pollution prediction models and contribute to subsequent health analyses.

• Asian Journal of Atmospheric Environment
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• 제1권1호
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• pp.1-8
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• 2007

Vehicle exhaust is a dominant source of air pollutants in urban areas. Since people are easily exposed to vehicle exhaust particles while driving a car and/or traveling via public transportation, air pollution near traffic has been extensively studied in developed countries. In this paper, investigations on vehicle-related fine particulate air pollution at roadsides and on roads in Seoul, Korea were reviewed to understand air pollution near traffic. Comparison of $PM_{10}$ concentrations in Seoul showed that roadside air is more contaminated than urban air, implying that exposure levels near vehicular emissions are more critical to sensitive persons. Concentrations of ultrafine particles and BC (black carbon) at roadsides of Seoul fluctuate highly for short durations, responding to traffic situations. Diurnal variations of ultrafine particles and BC concentrations at roadsides seem to be affected by traffic volume, mixing layer height, and wind speed. Concentrations of ultrafine particles and BC decrease as distance from the road increases due to dilution during transport. On-road air pollution seems to be more severe than roadside air pollution in Seoul. Since nearby traffic air pollution has not been well understood in Seoul, further studies including various vehicular air pollutants and representative locations are needed.

• 한국대기환경학회지
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• 제32권6호
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• pp.575-582
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• 2016

Seoul city has high population density as well as high traffic congestion, which are vulnerable to exposure of environmental pollutions caused by car traffic. However, recent studies are only on local regions about road traffic and air pollution or health effect of road traffic on residents. Thus, comprehensive study data are needed in terms of overall Seoul regions. In this study utilized the nitrogen dioxide concentration through the national air pollution monitoring network data, 2012 to 2013. It also divided regions into high and low exposure districts via the Origin destination data developed by the Korea transport institute to quantify and evaluate the effect of transport policies and analyzed a correlation of asthma symptoms with high and low exposure districts through raw data of community health survey from the Korea centers for disease control and prevention. Based on the collected data, the pearson's correlation analysis was conducted between air pollution substance concentration and high exposure district and multiple logistic regression analysis was conducted to determine the effect of traffic environment and factors on asthma symptoms of residents. Accordingly, the following results were derived. First, the high exposure district was higher concentrations of nitrogen dioxide ($NO_2$) as per time compared to those of the low exposure district (p<0.01). Second, analysis on correlation between average daily environmental concentration in the air pollution monitoring network and road traffic showed that nitrogen dioxide had a significant positive correlation (p<0.01) with car traffic and total traffic as well as with truck traffic (p<0.05) statistically. Third, an adjusted odds ratio about asthma doctor's diagnosis in the high and low exposure districts was analyzed through the logistic regression analysis. With regard to an adjusted model 2 (adjusted gender, age, health behavior characteristics, and demographic characteristics) odds ratio of asthma doctor's diagnosis in the high exposure district was 1.624 (95% CI: 1.269~2.077) compared to that of the low exposure district, which was significant statistically (p<0.001).

• Asian Journal of Atmospheric Environment
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• 제3권1호
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• pp.9-18
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• 2009

This study aimed at assessing the association between exposure to severe air pollution in the past and the subsequent transition in lung cancer mortality among persons who lived in an industrial city. Vital statistics from 1983 to 2006 and the data on air pollution measurements from 1960 to 1990 in Amagasaki City, Japan, were used. Pearson correlation coefficients were calculated between the standardized mortality ratios (SMRs) for lung cancer and the air pollution levels in 6 wards of Amagasaki City. The associations between changes in air pollution levels and the annual SMRs were also evaluated in the light of a potential latency period. The levels of air pollution were extremely high in the 1960s, and they decreased since 1970. The SMRs for lung cancer in 1989-1993 among females for 6 wards were significantly associated with the amounts of both sulfur oxides and dust fall in the past for each ward. The positive associations were observed between the annual SMRs among females and the amounts of both pollutants when the lag time of 20-30 years was taken into account. These results suggest that severe air pollution in the 1960s in an industrial city affected the subsequent increase in lung cancer mortality.

• 한국환경보건학회지
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• 제33권4호
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• pp.227-234
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• 2007

Recent epidemiologic studies show that gestational exposure to air pollution adversely affects pregnancy outcomes including low birth weight in preform birth. In this study, we evaluated the effect of air pollutants on LBW (low birth weight) on firstborn fetus throughout the gestational period using the birth cohort between 1999 and 2003 in Seoul. Using birth cohort data from the National Statistics Office of Korea we identified 288,346 firstborn births (excluded missing data on lack of information for birth weight and discordance between residential and certificated address from a total of 316,451) during 1999 to 2003 with complete covariate (gender, parity, date of birth, gestational age, parental age and educational level, maternal occupation etc.) and maternal residential history data. Our subjects were defined as more than 37 weeks and less than 44 weeks of completed gestation and we identified 5,457 persons (1.89%) by low birth weight (<2.5 kg) in this study. Using logistic regression, we estimated the risk of mean (entire pregnancy and trimester period) air pollution concentrations for CO, $O_3,\;PM_{10},\;NO_2\;and\;SO_2$. In terms of trimester-specific exposure, we found that some air pollutants exposure in each trimester would increase the risk for LBW. Results also showed that the effect size of air pollutants exposure during the first and third trimester is higher than during the second trimester. In all trimester, the estimated risk of LBW was 1.831 (95% CI=1.573-2.132) with unit increase for CO, 1.139 (95% CI=1.107-1.172) for 50, and 1.009 (95% CI=1.001-1.017) for $O_3$. Our results suggest that exposure during the gestation period to relatively low levels of some air pollutants may be associated with a reduction in birth weight on first-born fetus. These findings implicate the effective risk management strategies should be applied to minimize the public health impacts for pregnant women.

• Journal of Preventive Medicine and Public Health
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• 제51권5호
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• pp.263-264
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• 2018

Based on epidemiological and genomic characteristics, lung cancer in never smokers (LCNS) is a different disease from lung cancer in smokers. Based on current research, the main risk factor for LCNS may be air pollution. A recent case-control study in Koreans reported that nitrogen dioxide ($NO_2$) may be a risk factor for LCNS. Additionally, a cohort study showed that exposure to $NO_2$ was associated with significant hypomethylation. Thus, epigenetic epidemiology studies are needed in the near future to evaluate the carcinogenesis of LCNS according to chronic exposure to air pollution and/or viral infections.

• Clinical and Experimental Pediatrics
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• 제55권6호
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• pp.185-192
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• 2012

The prevalence of allergic diseases has increased worldwide, a phenomenon that can be largely attributed to environmental effects. Among environmental factors, air pollution due to traffic is thought to be a major threat to childhood health. Residing near busy roadways is associated with increased asthma hospitalization, decreased lung function, and increased prevalence and severity of wheezing and allergic rhinitis. Recently, prospective cohort studies using more accurate measurements of individual exposure to air pollution have been conducted and have provided definitive evidence of the impact of air pollution on allergic diseases. Particulate matter and ground-level ozone are the most frequent air pollutants that cause harmful effects, and the mechanisms underlying these effects may be related to oxidative stress. The reactive oxidative species produced in response to air pollutants can overwhelm the redox system and damage the cell wall, lipids, proteins, and DNA, leading to airway inflammation and hyper-reactivity. Pollutants may also cause harmful effects via epigenetic mechanisms, which control the expression of genes without changing the DNA sequence itself. These mechanisms are likely to be a target for the prevention of allergies. Further studies are necessary to identify children at risk and understand how these mechanisms regulate gene-environment interactions. This review provides an update of the current understanding on the impact of air pollution on allergic diseases in children and facilitates the integration of issues regarding air pollution and allergies into pediatric practices, with the goal of improving pediatric health.

• 치위생과학회지
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• 제24권1호
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• pp.22-28
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• 2024

Background: Particulate matter (PM) has been extensively observed due to its negative association with human health. Previous research revealed the possible negative effect of air pollutant exposure on oral health. However, the predictive model between air pollutant exposure and the prevalence of periodontitis has not been observed yet. Therefore, this study aims to propose a predictive model for the number of patients with periodontitis exposed to PM and atmospheric factors in South Korea using deep learning. Methods: This study is a retrospective cohort study utilizing secondary data from the Korean Statistical Information Service and the Health Insurance Review and Assessment database for air pollution and the number of patients with periodontitis, respectively. Data from 2015 to 2022 were collected and consolidated every month, organized by region. Following data matching and management, the deep neural networks (DNN) model was applied, and the mean absolute percentage error (MAPE) value was calculated to ensure the accuracy of the model. Results: As we evaluated the DNN model with MAPE, the multivariate model of air pollution including exposure to PM_2.5, PM₁₀, and other atmospheric factors predict approximately 85% of the number of patients with periodontitis. The MAPE value ranged from 12.85 to 17.10 (mean±standard deviation=14.12±1.30), indicating a commendable level of accuracy. Conclusion: In this study, the predictive model for the number of patients with periodontitis is developed based on air pollution, including exposure to PM_2.5, PM₁₀, and other atmospheric factors. Additionally, various relevant factors are incorporated into the developed predictive model to elucidate specific causal relationships. It is anticipated that future research will lead to the development of a more accurate model for predicting the number of patients with periodontitis.

• Annals of Occupational and Environmental Medicine
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• 제35권
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• pp.13.1-13.12
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• 2023

Background: Indoor air pollution can cause and exacerbate asthma. We report a previously undescribed case of occupational asthma related to indoor air pollution in a worker at an indoor air gun shooting range and highlight the potential risk of developing occupational asthma in this environment. Case presentation: A 31-year-old man presented with dyspnea, cough, and sputum and was diagnosed with asthma complicated by pneumonia. Objective evidence of asthma was obtained by performing a methacholine bronchial provocation test. It was suspected that the patient had occupational asthma, which began one month after changing jobs to work within the indoor air gun shooting range. The highest peak expiratory flow (PEF) diurnal variability on working days was 15%, but the highest variation was 24%, with 4 days out of 4 weeks having a variation of over 20% related to workplace exposure. Conversely, the diurnal variability on the rest days was 7%, and no day showed a variation exceeding 20%. The difference in the average PEF between working and rest days was 52 L/min. PEF deterioration during working days and improvement on rest days were noted. Conclusions: The results obtained from the in-depth analysis of the PEF were adequate to diagnose the patient with occupational asthma. Exposure to indoor air pollution and lead and the patient's atopy and allergic rhinitis may have contributed to the development of occupational asthma.

• 한국대기환경학회지
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• 제25권4호
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• pp.339-346
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• 2009

The objectives of this study were to introduce spatial interpolation methods which have been applied in recent papers, to apply three methods (nearest monitor, inverse distance weighting, kriging) to domestic data (Ulsan cohort) as an example of estimating the personal exposure levels. We predicted the personal exposure estimates of 2,102 participants in Ulsan cohort using spatial interpolation methods based on information of their residential address. We found that there was a similar tendency among the estimates of each method. The correlation coefficients between predictions from pairs of interpolation methods (except for the correlation coefficient between nearest montitor and kriging of CO and $SO_2$) were generally high (r=0.84 to 0.96). Even if there are some limitations such as location and density of monitoring station, spatial interpolation methods can reflect spatial aspects of air pollutant and spatial heterogeneity in individual level so that they provide more accurate estimates than monitor data alone. But they may still result in misclassification of exposure. To minimize misclassification for better estimates, we need to consider individual characteristics such as daily activity pattern.