• Title/Summary/Keyword: Hazardous Air Pollutants

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Contamination Characteristics of Hazardous Air Pollutants in Particulate Matter in the Atmosphere of Ulsan, Korea (울산시 미세먼지의 유해대기오염물질 오염 특성)

  • Lee, Sang-Jin;Kim, Seong-Joon;Park, Min-Kyu;Cho, In-Gyu;Lee, Ho-Young;Choi, Sung-Deuk
    • Journal of Environmental Analysis, Health and Toxicology
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    • v.21 no.4
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    • pp.281-291
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    • 2018
  • Recently, long-range atmospheric transport (LRAT) from China is regarded as a major reason for elevated levels of particulate matter (PM) in Korea. However, local emissions also play an important role in PM pollution, especially in large-scale industrial cities. In this study, PM samples were collected at suburban, residential, and industrial sites in Ulsan, Korea. Polycyclic aromatic hydrocarbons (PAHs) and heavy metals were analyzed, and a potential human health risk assessment was conducted. The concentrations of PAHs and heavy metals in total suspended particles (TSP) increased during high $PM_{10}$ episodes, and backward trajectory analysis verified the influence of LRAT from China during the high episodes. Furthermore, the concentrations of PAHs and heavy metals in $PM_{2.5}$ and $PM_{10}$ at the industrial site were higher than those at the residential site. The risk assessment of PAHs and heavy metals in $PM_{2.5}$ suggested no significant health effects. The highest levels of PAHs were measured in the particle size of $0.32{\sim}0.56{\mu}m$ at the residential site, and those of heavy metals were detected in the particle size of 1.8~5.6 and $>18{\mu}m$, reflecting different major emissions sources for both groups. On the basis of this preliminary study, we are planning long-term monitoring and modeling studies to quantitatively evaluate the influence of industrial activities on the PM pollution in Ulsan.

Air Pollution and Its Effects on E.N.T. Field (대기오염과 이비인후과)

  • 박인용
    • Proceedings of the KOR-BRONCHOESO Conference
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    • 1972.03a
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    • pp.6-7
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    • 1972
  • The air pollutants can be classified into the irritant gas and the asphixation gas, and the irritant gas is closely related to the otorhinolaryngological diseases. The common irritant gases are nitrogen oxides, sulfur oxides, hydrogen carbon compounds, and the potent and irritating PAN (peroxy acyl nitrate) which is secondarily liberated from photosynthesis. Those gases adhers to the mucous membrane to result in ulceration and secondary infection due to their potent oxidizing power. 1. Sulfur dioxide gas Sulfur dioxide gas has the typical characteristics of the air pollutants. Because of its high solubility it gets easily absorbed in the respiratory tract, when the symptoms and signs by irritation become manifested initially and later the resistance in the respiratory tract brings central about pulmonary edema and respiratory paralysis of origin. Chronic exposure to the gas leads to rhinitis, pharyngitis, laryngitis, and olfactory or gustatory disturbances. 2. Carbon monoxide Toxicity of carbon monoxide is due to its deprivation of the oxygen carrying capacity of the hemoglobin. The degree of the carbon monoxide intoxication varies according to its concentration and the duration of inhalation. It starts with headache, vertigo, nausea, vomiting and tinnitus, which can progress to respiratory difficulty, muscular laxity, syncope, and coma leading to death. 3. Nitrogen dioxide Nitrogen dioxide causes respiratory disturbances by formation of methemoglobin. In acute poisoning, it can cause pulmonary congestion, pulmonary edema, bronchitis, and pneumonia due to its strong irritation on the eyes and the nose. In chronic poisoning, it causes chronic pulmonary fibrosis and pulmonary edema. 4. Ozone It has offending irritating odor, and causes dryness of na sopharyngolaryngeal mucosa, headache and depressed pulmonary function which may eventually lead to pulmonary congestion or edema. 5. Smog The most outstanding incident of the smog occurred in London from December 5 through 8, 1952, because of which the mortality of the respiratory diseases increased fourfold. The smog was thought to be due to the smoke produced by incomplete combustion and its byproduct the sulfur oxides, and the dust was thought to play the secondary role. In new sense, hazardous is the photochemical smog which is produced by combination of light energy and the hydrocarbons and oxidant in the air. The Yonsei University Institute for Environmental :pollution Research launched a project to determine the relationship between the pollution and the medical, ophthalmological and rhinopharyngological disorders. The students (469) of the "S" Technical School in the most heavily polluted area in Pusan (Uham Dong district) were compared with those (345) of "K" High School in the less polluted area. The investigated group had those with subjective symptoms twice as much as the control group, 22.6% (106) in investigated group and 11.3% (39) in the control group. Among those symptomatic students of the investigated group. There were 29 with respiratory symptoms (29%), 22 with eye symptoms (21%), 50 with stuffy nose and rhinorrhea (47%), and 5 with sore thorat (5%), which revealed that more than half the students (52%) had subjective symptoms of the rhinopharyngological aspects. Physical examination revealed that the investigated group had more number of students with signs than those of the control group by 10%, 180 (38.4%) versus 99 (28.8%). Among the preceding 180 students of the investigated group, there were 8 with eye diseases (44%), 1 with respiratory disease (0.6%), 97 with rhinitis (54%), and 74 with pharyngotonsillitis (41%) which means that 95% of them had rharygoical diseases. The preceding data revealed that the otolaryngological diseases are conspicuously outnumbered in the heavily polluted area, and that there must be very close relationship between the air pollution and the otolaryngological diseases, and the anti-pollution measure is urgently needed.

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Data-centric XAI-driven Data Imputation of Molecular Structure and QSAR Model for Toxicity Prediction of 3D Printing Chemicals (3D 프린팅 소재 화학물질의 독성 예측을 위한 Data-centric XAI 기반 분자 구조 Data Imputation과 QSAR 모델 개발)

  • ChanHyeok Jeong;SangYoun Kim;SungKu Heo;Shahzeb Tariq;MinHyeok Shin;ChangKyoo Yoo
    • Korean Chemical Engineering Research
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    • v.61 no.4
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    • pp.523-541
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    • 2023
  • As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.