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Background: PM_2.5 and black carbon (BC) can be generated from cooking and from vehicle operation. Street vendors may be exposed to PM_2.5 and BC due to their proximity both to roads and to cooking activities.
Objectives: The objectives of this study were to evaluate the PM_2.5 and BC concentrations in cooking stalls and to determine the effects of cooking activity and of types of cooking.
Methods: Indoor and outdoor PM_2.5 and BC concentrations, temperature, and relative humidity were measured in 32 stalls in April and May 2022. Behavioral factors such as the presence of cooking activity and types of cooking were observed. Student’s T-test was performed using the difference of indoor and outdoor PM_2.5 and BC concentrations to compare the effects of cooking activity and to compare types of cooking.
Results: One-hour averages of the difference in indoor and outdoor PM_2.5 concentrations for cooking stalls and non-cooking stalls were 9.7±15.7 μg/m³ (n=22) and –0.5±0.4 μg/m³ (n=10), respectively. The difference in indoor and outdoor PM_2.5 concentrations in cooking stalls was significantly higher than in non-cooking stalls (p<0.05). The indoor PM_2.5 concentration for stalls for Chinese pancakes and teokbokki exceeded the standards for indoor air quality in South Korea (50 μg/m³). The indoor PM_2.5 concentration for Korean pancake stalls exceeded the standards for outdoor air quality in South Korea (35 μg/m³ for 24 hours).
Conclusions: The PM_2.5 concentrations in stalls with cooking activity was significantly higher than those in stalls without cooking activity. Some stalls with certain types of foods exceeded standards for indoor and outdoor air quality in South Korea. Better management of indoor air quality in stalls with cooking activities is necessary.

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Background: The concentration of air pollutants as measured by the Air Quality Monitoring System (AQMS) is not an accurate population exposure level since actual human activities and temporal and spatial variability need to be considered. Therefore, to increase the accuracy of exposure assessment, the population should be considered. However, it is difficult to obtain population data due to limitations such as personal information.
Objectives: The existing population defined in this study is the number of people in each region's grid. The purpose is to provide a methodology for evaluating exposure to PM_2.5 through existing population data provided by the National Geographic Information Institute.
Methods: The selected study period was from October 26 to October 28, 2021. Using PM_2.5 concentration data measured at the Sensor-based Air Monitoring Station (SAMS) installed in Guro-gu and Wonju-si, the concentration for each grid was estimated by applying inverse distance weights through QGIS version 3.22. Considering the existing population, population-weighted average concentration (PWAC) was calculated and the exposure level of the population was compared by region.
Results: The outdoor PM_2.5 concentration as measured through the SAMS was high in Wonju-si on all three days. Wonju-si showed an average 22% higher PWAC than Guro-gu. As a result of comparing the PWAC and outdoor PM_2.5 concentration by region, the PWAC in Guro-gu was 1~2% higher than the observed value, but it was almost the same. Conversely, observations of Wonju-si were 10.1%, 11.3%, and 8.2% higher than PWAC.
Conclusions: It is expected that the Geographic Information System (GIS) method and the existing population will be used to evaluate the exposure level of a population with a narrow activity radius in further research. In addition, based on this study, it is judged that research on exposure to environmental pollutants and risk assessment methods should be expanded.

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Background: Due to the rapid aging of the South Korean population, neurological diseases such as dementia are increasing. Many studies have reported that the incidence of dementia is associated with environmental factors along with age.
Objectives: This study analyzed the association between cognitive function and ten heavy metals in the body: arsenic, aluminum, chromium, manganese, cobalt, nickel, iron, copper, zinc, and lead.
Methods: From 2018 to 2019, a total of 120 participants who suffered from cognitive impairment were recruited for this study. Blood and urine samples were collected and analyzed for heavy metal concentrations using an inductively coupled plasma mass spectrometer. Demographic information was obtained through face-to-face questionnaires completed by a trained investigator. Cognitive function was evaluated with the Korean version of the Mini-Mental State Examination and the Korean version of the Boston Name Waiting Test. The associations between cognitive function scores and heavy metal concentrations were analyzed using multiple logistic regression analysis.
Results: The average age of the 120 participants was 72.7 years, and 69.2% were female. The mean of the MMSE-K and K-BNT scores were 22.9 and 37.9, respectively. The geometric mean of aluminum (Al) was 8.42 μg/L. MMSE-K was associated with iron (Fe), but the significance was removed in the logistic regression based on 24 points. K-BNT was significantly associated with aluminum and the odds ratio for K-BNT above 38 decreased by 45% as the aluminum concentration increased.
Conclusions: The association between aluminum and the K-BNT score indicated that aluminum is associated with language-related cognitive decline. Based on this result, further study will be conducted by considering co-exposure effects of heavy metals including aluminum.

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Background: Socioeconomical disadvantaged communities are more vulnerable to environmental chemical exposure and associated health effects. However, there is limited information on chemical exposure among vulnerable populations in Korea.
Objectives: This study investigated chemical exposure among underprivileged populations. We measured urinary metabolites of phthalates in urban disadvantaged communities and investigated their correlations with residential environment factors and relative socioeconomic vulnerability.
Methods: Urine samples were collected from 64 residents in a disadvantaged community in Seoul. A total of eight phthalate metabolites were analyzed by liquid chromatography-mass spectroscopy. Analytical method used by the Korean National Environmental Health Survey (KoNEHS) was employed. Covariate variance analysis and general linear regression adjusted with age, sex and smoking were performed.
Results: Several phthalate metabolites, namely monomethyl phthalate (MMP), monoethyl phthalate (MEP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono-n-butyl phthalate (MnBP) had higher levels than those reported in the adults of 4th KoNEHS. Notably, the MnBP level was higher in the lower socioeconomic group (geometric mean [GM]=47.3 μg/g creatinine) compared to non-recipients (GM=31.9 μg/ g creatinine) and the national reference level (GM=22.0, 28.2 and 32.2 μg/g creatinine for adults, 60’s and 70’s, respectively.). When age, sex and smoking were adjusted, MEP and MnBP were significantly increased the lower socioeconomic group than non-recipients (p=0.014, p=0.023). The lower socioeconomic group’s age of flooring were higher than non-recipients, not statistically significant.
Conclusions: These results suggest that a relatively low income and aged flooring could be considered as risk factors for increased levels of phthalate metabolites in socioeconomic vulnerable populations.

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Background: Lung injuries due to exposure to humidifier disinfectants (HDs) were reported in 2011 in South Korea. As a result of the government’s epidemiological investigation and toxicity test study, it was found that HDs caused health damage such as lung disease.
Objectives: The purpose of this study was to classify HD exposure ratings and analyze the affecting factors that could identify the relationship with lung disease.
Methods: Exposure assessment for HDs was conducted using a questionnaire during face-to-face interviews with the applicants. Ratings of high exposure (Class 1) and low exposure (Class 2) were cross-tabulated with clinical ratings (acceptable and unacceptable). Logistic regression analysis was carried out by setting the clinical rating of lung disease as a dependent variable and the socio-demographic and exposure characteristics obtained through the questionnaire as independent variables.
Results: The concentration in air of polyhexamethylene guanidine (PHMG) was 71.96±107.47 μg/m³, and the exposure concentration was 15.21±23.28 μg/m³. The exposure rating was overestimated with 97.1% of affected subjects having high exposure using margin of exposure (MOE), but only 9.9% matching the clinical class. In the overestimated group, it could be explained by the fact that the exposure time was long and the subjects had already recovered from damage symptoms. As a result of logistic regression analysis, ten variables were found to be significant influencing factors.
Conclusions: A new exposure rating could be calculated based on the MOE, and factors affecting lung disease could be estimated through comparative evaluation with the clinical rating.