• The Korean Journal of Malacology
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• v.25 no.3
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• pp.213-222
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• 2009

In order to investigate contamination of heavy metal in seawater and cultured oyster, samples were collected November 2003 to July 2004 from 12 sites (13 sites for seawater) along the coast of Tongyeong, Korea. The mean concentrations of metal in oyster tissues were as follows: 0.09 (0.01-0.3) ${\mu}g/l$ for Cd, 0.47 (0.01-1.4) ${\mu}g/l$ for Cr, 0.59 (0.2-2.3) ${\mu}g/l$ for Ni, 1.02 (0.1-4.2) ${\mu}g/l$ for Pb and 0.48 (0.01-3.9) ${\mu}g/l$ for Hg in the seawater, whereas 2.45 (0-5.47) mg/kgDW for Cd, 3.63 (0.10-12.91) mg/kgDW for Cr, 3.2 (0.01-15.73) mg/kgDW for Ni, 3.51 (0.01-6.47) mg/kgDW for Pb and 0.39 (0.004-0.74) mg/kgDW for Hg, respectively. Most metal concentration values were below the permissible range for the related regulations. Mean bioconcentration factors (BCF) for each metal were as follows: 38,964 (1,771-207, 171) for Cd, 9,583 (1,231-80, 162) for Cr, 191 (3-20, 980) for Ni, 1,416 (245-5, 207) for Pb and 180 (5-716) for Hg, respectively. The BCF values from this study corresponded to the transitional phase from the pristine to the contaminated waters. Notably, Cd showed the highest BCF, which suggest that the Pacific oyster could be utilized as a useful biomarker for Cd contamination in sea water. The multidimensional scaling analysis suggested that the metal contaminants are mainly originated from combustion of fossil fuel and accumulated to oyster through food web.

• Korean Journal of Environmental Agriculture
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• v.35 no.4
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• pp.278-285
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• 2016

BACKGROUND: Uptake patterns of ${\alpha}$-, ${\beta}$-isomers and sulfate metabolite of endosulfan (ED) by radishes grown in treated soils with ED concentrations of 2 and 10 mg/kg were investigated to establish soil management guidelines for ensuring the safety of radishes from ED residues. METHODS AND RESULTS: All samples of soils and radish plants separated into shoot and root parts were analyzed for ED residues using a gas-chromatography mass spectrophotometer, and the results were used to calculate the bioconcentration factor (BCF), indicating the ratio of ED concentrations between radishes and soils. During the experimental period, uptake and distribution rates of ED-sulfate in radishes were the highest, followed by ${\alpha}$- and ${\beta}$-ED. The BCF values to initial ED concentrations in soils were greater for root parts (0.0077 to 0.2345) than for shoot parts (0.0002 to 0.0429) and used to obtain regression equations by time. Long-term BCFs estimated by the obtained equations ($R^2$ of 0.86 to 1.00) were evaluated with the maximum residue limit (0.1 mg/kg) of ED for radishes, in order to suggest safe management guidelines of ED for radish-cultivating soils. CONCLUSION: Suggested guidelines showed the significant dependency on duration for radish cultivation and exposed concentration of ED in soil.

• Korean Journal of Environmental Agriculture
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• v.32 no.3
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• pp.224-230
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• 2013

BACKGROUND: Perchlorate(${ClO_4}^-$) is an anion that is extremely water-soluble and environmentally stable. It mostly exists in the form of sodium perchlorate, ammonium perchlorate and potassium perchlorate which are used in rocket fuels, propellants, ignitable sources, air bag inflation systems and explosives. Perchlorate can be taken into the thyroid glands and interfere with iodide uptake. The determination of perchlorate in agricultural products is important due to its potential health impact on humans. The objective of this study was to determine the perchlorate concentrations in the samples of various agricultural products and soils. METHODS AND RESULTS: In this study, samples of cereal(Rice, Barley, Corn, Bean), vegetable(Spinach, Lettuce, Sesame, Chives, Chili, Pumpkin, Tomato), fruit(Apple, Pear, Tangerine, Grape) were analyzed for perchlorate contents. Perchlorate concentrations were analyzed by liquid chromatography-tandem mass spectrometry. The results showed that agricultural products respectively contained perchlorate concentrations in the range of : cereals N.D.~$7.46{\mu}g/kg$, vegetables $0.52{\sim}23.06{\mu}g/kg$, fruits $0.19{\sim}2.66{\mu}g/kg$. Bioconcentration factor was in the order of : vegetables > cereals > fruits. Bioconcentration factor was highest follwed by Sesame 37.88, Corn 21.51, Spinach 10.57, Tangerine 4.39, Chives 2.89 and Lettuce 1.90. The recoveries of perchlorate from spiked agricultural products and soils ranged from 87.72~111.26% and 102.09~111.23%. CONCLUSION(S): The health risk assessment results obtained in this study are lower than the RfD(Reference Dose, 0.0007 mg/kg/body weight/day) value as suggested by the Integrated Risk Information System(US IRIS). Our results indicate that, people currently exposed to perchlorate from agricultural products consumption are considered as safe.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.11
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• pp.947-956
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• 2009

Residual petroleum hydrocarbons after an oil spill may accumulate in the marine benthic ecosystem due to their high hydrophobicity. A lot of monitoring data are required for the estimation of ecosystem exposure to residual petrochemicals in an ecological risk assessment in the affected region. To save time and cost, the environmental exposure to them in the affected ecosystem can also be assessed using a simple food-web bioaccumulation model. In this study, we evaluated residual concentrations of four selected polycyclic aromatic hydrocarbons (phenanthrene, anthracene, pyrene, and benzo[a]pyrene) in a hypothetic benthic ecosystem composed of six species under two exposure scenarios. Body-residue concentration ranged 5~250 mg/kg body depending on trophic positions in an extreme scenario in which the aqueous concentrations of PAHs were assumed to be one-tenth of their aqueous solubility. In addition, bioconcentration factors (BCFs) and bioaccumulation factors (BAFs) were evaluated for model species. The logarithm of bioconcentration factor (log BCF) linearly increased with increasing the logarithm of 1-octanol-water partition coefficient (log $K_{OW}$) until log $K_{OW}$ of 7.0, followed by a gradual decrease with further increase in log $K_{OW}$ without metabolic degradation. Biomagnification became significant when log $K_{OW}$ of a pollutant exceeded 5.0 in the model ecosystem, indicating that investigation of food-web structure should be critical to predict biomagnifications in the affected ecosystem because log $K_{OW}$ values of many petrochemicals are higher than 5.0. Although further research is required for better site-specific evaluation of exposure, the model simulation can be used to estimate the level of the ecosystem exposure to residual oil contaminants at the screening level.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.515-521
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• 2015

There is an increasing concern over heavy metal(loid) contamination of soil in agricultural areas including paddy soils. This study was conducted to monitor the background levels of heavy metal(loid)s, arsenic (As), cadmium (Cd), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) in major rice growing soils and its accumulation in brown rice in Korea. The samples were collected from 82 sites nationwide in the year 2012. The mean and range values of As, Cd, Cu, Hg, Ni, Pb, and Zn in paddy soils were 4.41 (0.16-18.9), 0.25 (0.04-0.82), 13.24 (3.46-27.8), 0.047 (0.01-0.20), 13.60 (3.78-35.0), 21.31 (8.47-36.7), and 54.10 $(19.19-103.0)mg\;kg^{-1}$, respectively. This result indicated that the heavy metal(loid) levels in all sampled paddy soils are within the permissible limits of the Korean Soil Environment Conservation Act. The mean and range values of As, Cd, Cu, Hg, Ni, Pb, and Zn in brown rice were 0.146 (0.04-0.38), 0.024 (0.003-0.141), 4.27 (1.26-16.98), 0.0024 (0.001-0.008), 0.345 (0.04-2.77), 0.113 (0.04-0.197), and 22.64 $(14.1-35.1)mg\;kg^{-1}$, respectively. The mean and range BCF (bioconcentration factor) values of As, Cd, Cu, Hg, Ni, Pb, and Zn in brown rice were 0.101 (0.01-0.91), 0.121 (0.01-0.70), 0.399 (0.05-2.60), 0.061 (0.016-0.180), 0.033 (0.004-0.44), 0.005 (0.003-0.013), and 0.473 (0.19-1.07), respectively, with Zn showing the highest. The results show that the levels of all metal(loid)s in all sampled brown rice are generally within the acceptable limit for human consumption.

• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.414-431
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• 2016

There are numerous closed and abandoned mines in Korea, from which diverse heavy metals (e.g., As, Cd, Cu, Pb, Zn) are released into the surrounding soil, groundwater, surface water, and crops, potentially resulting in detrimental effects on the health of nearby residents. Therefore, we performed human risk assessments of two abandoned metal mines, Yanggok (YG) and Samsanjeil (SJ). The exposure parameters used in this assessment were specific to residents near mines and the included exposure pathways were relevant to areas around metal mines. The computed total excess carcinogenic risks for both areas exceeded the acceptable carcinogenic risk ($1{\times}10^{-6}$), indicating that these areas are likely unsafe due to a carcinogenic hazard. In contrast, the non-carcinogenic risks of the two areas differed among the studied receptors. The hazard indices were higher than the unit risk (=1.0) for male and female adults in YG and male adults in SJ, suggesting that there are non-carcinogenic risks for these groups in the study areas. However, the hazard indices for children in YG and female adults and children in SJ were lower than the unit risk. Consumption of groundwater and crops grown in the area were identified as major exposure pathways for carcinogenic and non-carcinogenic hazards in both areas. Finally, the dominant metals contributing to carcinogenic and non-carcinogenic risks were As and As, Cu, and Pb, respectively. In addition, the carcinogenic and non-carcinogenic risks of YG were evaluated to be 10 and 4 times higher than those of SJ, respectively, resulted from the relatively higher exposure concentration of As in groundwater within SJ area. Because of lacking of several exposure parameters, some of average daily dose (ADD) could not be computed in this study. Furthermore, it is likely that the ADDs of crop-intake pathway included some errors because they were calculated using soil exposure concentrations and bioconcentration factor (BCF) rather than using crop exposure concentrations.

• 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.

• Environmental Analysis Health and Toxicology
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• v.19 no.1
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• pp.33-40
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• 2004

Benzoyl peroxide is a High Production Volume Chemical, which is produced about 1,371 tons/year in Korea as of 2001 survey. The substance is mainly used as initiators in polymerization, catalysts in the plastics industry, bleaching agents for flour and medication for acne vulgaris. In this study, Quantitative Structure-Activity Relationships (QSAR) are used for getting adequate information on the physical -chemical properties of this chemical. And hydrolysis in water, acute toxicity to aquatic and terrestrial organisms for benzoyl peroxide were studied. The physical -chemical properties of benzoyl peroxide were estimated as followed; vapor pressure=0.00929 Pa, Log $K_{ow}$ = 3.43, Henry's Law constant=3.54${\times}$10$^{-6}$ atm-㎥/mole at $25^{\circ}C$, the half-life of photodegradation=3 days and bioconcentration factor (BCF)=92. Hydrolysis half-life of benzoyl peroxide in water was 5.2 hr at pH 7 at $25^{\circ}C$ and according to the structure of this substance hydrolysis product was expected to benzoic acid. Benzoyl peroxide has toxic effects on the aquatic organisms. 72 hr-Er $C_{50}$ (growth rate) for algae was 0.44 mg/1.,48 hr-E $C_{50}$ for daphnia was 0.07mg/L and the 96hr-L $C_{50}$ of acute toxicity to fish was 0.24mg/L. Acute toxicity to terrestrial organisms (earth worm) of benzoyl peroxide was low (14 day-L $C_{50}$ = > 1,000 mg/kg). Although benzoyl peroxide is high toxic to aquatic organisms, the substance if not bioaccumulated because of the rapid removal by hydrolysis (half-life=5.2 hr at pH 7 at $25^{\circ}C$) and biodegradation (83% by BOD after 21 days). The toxicity observed is assumed to be due to benzoyl peroxide rather than benzoic acid, which shows much lower toxicity to aquatic organisms. One can assume that effects occur before hydrolysis takes place. From the acute toxicity value of algae, daphnia and fish, an assessment factor of 100 was used to determine the predicted no effect concentration (PNEC). The PNEC was calculated to be 0.7$\mu\textrm{g}$/L based on the 48 hr-E $C_{50}$ daphnia (0.07 mg/L). The substance shows high acute toxicity to aquatic organisms and some information indicates wide-dispersive ore of this substance. So this substance is, a candidate for further work, even if it hydrolysis rapidly and has a low bioaccumulation potential. This could lead to local concern for the aquatic environment and therefore environmental exposure assessment is recommended.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.6
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• pp.464-472
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• 2014

The Codex Committee of Contaminants in Food (CCCF) has been discussing a new standard for arsenic (As) in rice since 2010 and a code of practice for the prevention and reduction of As contamination in rice since 2013. Therefore, our current studies focus on setting a maximum level of As in rice and paddy soil by considering bioavailability in the remediation of As contaminated soils. This study aimed to select an appropriate single chemical extractant for evaluating the mobility of As in paddy soil and the bioavailability of As to rice. Nine different extractants, such as deionized water, 0.01 M $Ca(NO_3)_2$, 0.1 M HCl, 0.2 M $C_6H_8O_7$, 0.43 M $HNO_3$, 0.43 M $CH_3COOH$, 0.5 M $KH_2PO_4$, 1 M HCl, and 1 M $NH_4NO_3$ were used in this study. Total As content in soil was also determined after aqua regia digestion. The As extractability of the was in the order of: Aqua regia > 1 M HCl > 0.5 M $KH_2PO_4$ > 0.43 M $HNO_3$ > 0.2 M $C_6H_8O_7$ > 0.1 M HCl > 0.43 M $CH_3COOH$ > deionized water > 1 M $NH_4NO_3$ > 0.01 M $Ca(NO_3)_2$. Correlation between soil extractants and As content in rice was in the order of : deionized water > 0.01 M $Ca(NO_3)_2$ > 0.43 M $CH_3COOH$ > 0.1 M HCl > 0.5 M $KH_2PO_4$ > 1 M $NH_4NO_3$ > 0.2 M $C_6H_8O_7$ > 0.43 M $HNO_3$ > 1M HCl > Aqua regia. BCF (bioconcentration factor) according to extractants was in the order of : 0.01M $Ca(NO_3)_2$ > 1 M $NH_4NO_3$ > deionized water > 0.43 M $CH_3COOH$ > 0.1 M HCl > 0.43 M $HNO_3$ > 0.2 M $C_6H_8O_7$ > 0.5 M $KH_2PO_4$ > 1 M HCl > Aqua regia. Therefore, 0.01 M $Ca(NO_3)_2$ ($r=0.78^{**}$) was proven to have the greatest potential for predicting As bioavailability in soil with higher correlation between As in rice and the extractant.