This study developed a QSAR regression model using the XGBoost machine learning algorithm to predict the acute aquatic toxicity of highly hazardous PCBs. EC50 values for Daphnia magna were obtained from QSAR Toolbox 4.7. Input features consisted of approximately 3,000 molecular descriptors and fingerprints generated from official structure data using RDKit and the Morgan algorithm, excluding mixtures. The dataset was split into training and test sets (7 : 3) based on 500,000 randomized seeds, and the most balanced combination was selected using Kolmogorov-Smirnov and Wilcoxon rank-sum tests. Z-score standardization was applied based on the training set, and the XGBoost model was trained using 5-fold cross-validation with grid search optimization. The final model showed excellent predictive performance (R2 =0.97, RMSE= 0.19). A simplified model using only the top 10 predictive molecular features retained approximately 95% of the original accuracy while improving interpretability and efficiency. The model was applied to 38 PCB compounds lacking EC50 values, and the predicted values showed a statistically similar distribution to the measured group, with only minor differences in a few structural fingerprints. These results demonstrate the applicability of XGBoost-based models for reliable toxicity prediction and offer a promising alternative approach for assessing the environmental risk of untested PCBs.

This study aimed to develop a model for accurately predicting the acute aquatic toxicity (48h- EC50) of chlorine disinfection by-products (DBPs). DBPs have caused environmental risks, but experimental toxicity data are difficult to obtain due to time, cost, and ethical constraints. Therefore, a deep learning model was developed using actual concentration-based data. Toxicity data for 139 aliphatic chlorinated compounds were from the OECD QSAR Toolbox and from aquatic toxicity test results provided by the japan ministry of the environment. Various concentration criteria, including nominal and measured concentrations, were encoded as additional inputs, and EC50 values were augmented via log transformation and structural string modifications to overcome small data limitations. The directed message passing neural network (D-MPNN) model, which considers bond directionality, was applied to reflect structural complexity accurately. Also, this model effectively reflected subtle structural differences and showed stable performance even with limited data. Comparisons between models with and without concentration criteria revealed that the model considering all concentration criteria had superior predictive accuracy. This result shows that concentration criteria are a critical factor in toxicity prediction. This study suggests a baseline model that works reliably even with small datasets reflecting realistic concentration criteria, showing its potential use for replacing some experiments and for screening toxic substances.

This study conducted an acute toxicity assessment using Daphnia magna to evaluate the potential for increased toxicity when pharmaceuticals persist in aquatic environments not as single substances but in mixed forms. In single-substance toxicity tests, the antibiotics clarithromycin and sulfamethoxazole showed EC50 values of 22.3 mg L-1 and 61.05 mg L-1, respectively. However, the EC50 for the mixture of the two substances was determined to be 31.1 mg L-1. Based on these findings, applying the Similar Mode of Action (MOA) equation from the QSAR Toolbox, as recommended by OECD non-testing methods, produced an estimated EC50 of 33.7 mg L-1 for the mixture, showing a difference of 8.5% compared to the experimental value. This study confirms that combined exposure to pharmaceuticals can increase toxicity due to synergistic effects, indicating a significant potential risk to aquatic ecosystems. According to the UN-GHS classification criteria, clarithromycin, sulfamethoxazole, and their mixture were all classified as Category 3, indicating potential toxicity to aquatic organisms. These results emphasize the importance of toxicity assessments that consider interactions between multiple contaminants in real environmental settings, contributing to the development of effective toxicity evaluation and management strategies for the protection of aquatic ecosystems.

고탁도 원수의 고도정수처리를 위해 관형 세라믹 정밀여과막 외부와 원통형 막 모듈 내부 사이의 공간에 광촉매를 충전한 혼성 모듈을 사용하였다. 광촉매는 PP (polypropylene) 구(bead)에 TiO2 분말을 플라즈마 화학증착(chemical vapor deposition) 공정으로 코팅한 것이다. 정수 원수 중 자연산 유기물(NOM)과 미세 무기 입자를 대체하기 위해, 휴믹산(humic acid)과 카올린(kaolin) 모사용액을 대상으로 하였다. 혼성공정에서 막오염을 최소화하기 위해 10분 주기로 10초 동안 물 역세척을 시행하였다. 휴믹산을 10 mg/L부터 2 mg/L로 변화시킴에 따라, 막오염에 의한 저항(Rf)이 감소하고 J가 증가하여 2 mg/L에서 가장 높은 총여과부피(VT)를 얻었다. 탁도 및 UV254 흡광도의 처리효율은 각각 98.5% 및 85.7% 이상이었다. MF 공정 및 MF + TiO2 공정, MF + TiO2 + UV 공정의 막여과 및 광촉매 흡착, 광산화의 처리 분율을 알아본 결과, 광촉매 흡착과 광산화에 의해 탁도는 거의 처리되지 않았으나, 광촉매 흡착 및 광산화에 의한 휴믹산 처리 분율은 각각 10.7, 8.6% 이상이었다.