• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.108-119
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• 2024

Objectives: Abrupt changes in air pollution levels associated with the coronavirus disease 2019 (COVID-19) outbreak present a unique opportunity to evaluate the effects of air pollution on influenza risk, at a time when emission sources were less active and personal hygiene practices were more rigorous. Methods: This time-series study examined the relationship between influenza cases (n=22 874) and air pollutant concentrations from 2018 to 2021, comparing the timeframes before and during the COVID-19 pandemic in and around Thailand's Khon Kaen province. Poisson generalized additive modeling was employed to estimate the relative risk of hospitalization for influenza associated with air pollutant levels. Results: Before the COVID-19 outbreak, both the average daily number of influenza hospitalizations and particulate matter with an aerodynamic diameter of 2.5 ㎛ or less (PM_2.5) concentration exceeded those later observed during the pandemic (p<0.001). In single-pollutant models, a 10 ㎍/m³ increase in PM_2.5 before COVID-19 was significantly associated with increased influenza risk upon exposure to cumulative-day lags, specifically lags 0-5 and 0-6 (p<0.01). After adjustment for co-pollutants, PM_2.5 demonstrated the strongest effects at lags 0 and 4, with elevated risk found across all cumulative-day lags (0-1, 0-2, 0-3, 0-4, 0-5, and 0-6) and significantly greater risk in the winter and summer at lag 0-5 (p<0.01). However, the PM_2.5 level was not significantly associated with influenza risk during the COVID-19 outbreak. Conclusions: Lockdown measures implemented during the COVID-19 pandemic could mitigate the risk of PM_2.5-induced influenza. Effective regulatory actions in the context of COVID-19 may decrease PM_2.5 emissions and improve hygiene practices, thereby reducing influenza hospitalizations.

• Journal of Environmental Impact Assessment
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• v.18 no.1
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• pp.31-39
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• 2009

A lot of data which are used in environment analysis of air pollution have characteristics that are distributed continuously in space. In this point, the collected data value such as precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When geostatistical data analysis are needed, acquisition of the value in every point is the best way, however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the unknown values at unsampled locations based on observations. In this study, spatial interpolation method such as local trend surface model, IDW(inverse distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation accuracy, range of estimated value, RMSE, average error were analyzed with observation data. The Kriging and RBF methods had the higher accuracy than others.

• Advances in environmental research
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• v.10 no.1
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• pp.17-41
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• 2021

Kazakhstan's cities experience high concentrations levels of atmospheric particulate matter (PM), which is well-known for its highly detrimental effect on the human health. A further increase in PM concentrations in the future could lead to a higher air pollution-caused morbidity and mortality, causing an increase in healthcare expenditures by the government. However, to prevent elevated PM concentrations in the future, more stringent standards could be implemented by lowering current maximum allowable PM concentration limit to Organization for Economic Co-operation and Development (OECD)'s limits. Therefore, this study aims to find out what impact this change in environmental policy towards PM has on state economy in the long run. Future PM10 and PM2.5 concentrations were estimated using multiple linear regression based on gross regional product (GRP) and population growth parameters. Dose-response model was based on World Health Organization's approach for the identification of mortality, morbidity and healthcare costs due to air pollution. Analysis of concentrations revealed that only 6 out of 21 cities of Kazakhstan did not exceed the EU limit on PM10 concentration. Changing environmental standards resulted in the 71.7% decrease in mortality and 77% decrease in morbidity cases in all cities compared to the case without changes in environmental policy. Moreover, the cost of morbidity and mortality associated with air pollution decreased by $669 million in 2030 and $2183 million in 2050 in case of implementation of OECD standards. Thus, changing environmental regulations will be beneficial in terms of both of mortality reduction and state budget saving.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.750-761
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• 2008

The social and economical value assessment of railway facilities implies unaccountable values such as: reduction of travel time, reduction of car accident, reduction of air pollution. However, the value of railway facilities has been evaluated as the transportation and management results. The investment and management results of railway need to be considered objectively. Therefore, this study established the economical value assessment method of railway facilities composed of 10 items of economical value through the existing a preliminary assessment investigation, a manual of railway investment assessment, paper study. In addition, this study proposed the strategy for the value assessment of railway facilities before and after.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.71-81
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• 2015

Objectives: Photochemical ozone pollution is associated with increased mortality risk. This study aims to assess the population exposure to ozone according to population characteristics for high ozone days in the Busan metropolitan region (BMR). Methods: The ozone exposure assessment in this study was performed using the WRF-CMAQ simulated ozone concentrations and the population data in the BMR. The settled and daytime population and their activity were considered to conduct the static and dynamic ozone exposure assessment. Results: Applying a static exposure assessment, in case that high ozone occurred throughout Busan area, the highest exposure levels were evaluated in urban neighborhoods. In case of ozone pollution in outer Busan, because sensitive groups have been relatively higher exposure, this case was also evaluated as part of that should not be overlooked. The dynamic exposure was higher than static exposure because the number of population exposed to ozone of high concentration is increased. This approach is important in a regard consider that daytime population distribution when high ozone occur. Conclusion: This study shows the different population exposure according to various ozone distributions for each episode day. Considering demographic characteristic such as population density and activity should be important to understanding the population exposure assessment when ozone pollution occurs.

• Journal of dental hygiene science
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• v.24 no.1
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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.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1635-1641
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• 2020

Air pollution is one of the most severe threats to society globally due to the rapid expansion of urbanization and industrialization. Particularly, particulate matter (PM) pollution was recently designated as a social disaster by the Korean government because of increases in public concerns and the accumulation of scientific evidence that links high levels of PM2.5 (PM smaller than 2.5 ㎛ in diameter) to a long list of adverse health effects. Atmospheric PM concentrations can also affect the indoor PM levels to which people are exposed most of the time. Thus, understanding the characteristics of indoor and ambient PM pollution based on measurements, model simulations, risk assessments, and management technologies is inevitable in establishing effective policies to mitigate social, economic, and health costs incurred by PM pollution. In this special issue, we introduce several interesting studies concerning indoor and outdoor PM from the perspective of monitoring, assessment, and management being conducted by i-SEED (School of Integrated Science for Sustainable Earth & Environmental Disaster at Pukyong National University) and SPMC (School Particulate Matter Center for Energy and Environmental Harmonization). We expect that this special issue can improve our understanding of the current and future of indoor and outdoor PM pollution, integrating the results from interdisciplinary research groups from various academic fields.

• Journal of Radiation Protection and Research
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• v.26 no.2
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• pp.79-86
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• 2001

This study presents the quantitative exposure assessment of indoor radon released from groundwater. Most of the Indoor radon comes directly from soil beneath the basement or foundation. Recently, radon in groundwater releases to indoor air whenever the water is used and contributes to the total inhalation risk from indoor air. This study first develops a mathematical model to describe the transfer and distribution of radon released from groundwater in a house. Then, daily human exposures through inhalation or such radon are estimated with the model for an male adult based on two sets of exposure scenarios. The results obtained from the study would help increase the understanding of risk assessment issues associated with the indoor radon released from groundwater.

• Asian Journal of Atmospheric Environment
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• v.11 no.2
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• pp.61-70
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• 2017

This study evaluated the human health risk in terms of the excess number of mortality and morbidity in the megacity Mumbai, India due to air pollution. AirQ software was used to enumerate the various health impacts of critical pollutants in Mumbai in past 22 years during 1992-2013. A relationship concept based on concentration-response relative risk and population attributable-risk proportion was employed by adopting World Health Organization (WHO) guideline for concentrations of air pollutants like $PM_{10}$, $SO_2$ and $NO_2$. For the year 1992 in Mumbai, it was observed that excess number of cases of total mortality, cardiovascular mortality, respiratory mortality, hospital admission due to COPD, respiratory disease and cardiovascular disease were 8420, 4914, 889, 149, 10568 and 4081 respectively. However, after 22 years these figures increased to 15872, 9962, 1628, 580, 20527 and 7905 respectively, but all of these reached maximum in the year 2006. From the result, it is also noted that except COPD morbidity the excess number of cases from 1992-2002 to 2003-2013 increased almost by 30%; and the excess number of mortality and morbidity is basically due to particulate matter ($PM_{10}$) than due to gaseous pollutants.

• Asian Journal of Atmospheric Environment
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• v.12 no.3
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• pp.232-243
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• 2018

The construction, development and maintenance of an economically, environmentally and socially sustainable campus involves the integration of measuring tools and technical information that invites and encourages the community to know the actual state to generate positive actions for reducing the negative impacts over the local environment. At the Universidad Nacional de Colombia - Campus $Bogot{\acute{a}}$, a public area with daily traffic of more than 25000 people, the Environmental Management Bureau has committed with the monitoring of the noise pollution and air quality, as support to the campaigns aiming to reduce the pollutant emissions associated to the student's activities and campus operation. The target of this study is based in the implementation of mobile air quality and sonometry monitoring equipment, the mapping of the actual air quality and noise pollution inside the university campus as a novel methodology for a sub-area inside a megacity. This results and mapping are proposed as planning tool for the institution administrative sections. A mobile Kunak$^{(R)}$ Air & OPC air monitoring station with the capability to measure particulate matter $PM_{10}$, $PM_{2.5}$, Ozone ($O_3$), Sulfur Oxide ($SO_2$), Carbon Monoxide (CO) and Nitrogen Oxide ($NO_2$) as well as Temperature, Relative Humidity and Latitude and Longitude coordinates for the data georeferenciation; and a sonometer Cirrus$^{(R)}$ 162B Class 2 were used to perform the measurements. The measurements took place in conditions of academic activity and without it, with the aim of identify the impacts generated by the campus operation. Using the free code geographical information software QGIS$^{(R)}$ 2.18, the maps of each variable measured were developed, and the impacts generated by the operation of the campus were identified qualitative and quantitively. For the measured variables, an increase of around 21% for the $L_{Aeq}$ noise level and around 80% to 90% for air pollution were detected during the operation period.