Air pollutant measurement and respiratory inflammatory tests were conducted at a junior secondary school in Xi’an, Northwestern China. Hazardous substances including particulate matters (PMs), black carbon (BC) and particle-bounded polycyclic aromatic hydrocarbons (PAHs) were quantified both indoors and outdoors of the school. Source characterization with organic tracers and particle-size distribution demonstrated that the school’s air was mostly polluted by combustion emissions from the surrounding environment. The evaluation of health assessment related to air quality was conducted by two methods, including potential risk estimation of air pollutants and direct respiratory inflammatory test. The incremental lifetime cancer risks associated with PAHs were estimated and were 1.62 × 10−6 and 2.34 × 10−6, respectively, for indoor and outdoor fine PMs. Both the values exceeded the threshold value of 1 × 10−6, demonstrating that the carcinogenic PAHs are a health threat to the students. Respiratory inflammatory responses of 50 students who studied in the sample classroom were examined with a fractional exhaled nitric oxide (FeNO) test, with the aid of health questionnaires. The average FeNO concentration was 17.4 ± 8.5 ppb, which was slightly lower than the recommended level of 20 ppb established by the American Thoracic Society for children. However, a wide distribution and 6% of the values were > 35 ppb, suggesting that the potentials were still high for eosinophilic inflammation and responsiveness to corticosteroids. A preliminary interpretation of the relationship between air toxins and respiratory inflammatory response demonstrated the high exposure cancer risks and inflammatory responses of the students to PMs in the city.
● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting CODeff in wastewater was proposed. ● The CODeff prediction performances of the three models in the paper were compared. ● The CODeff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R2) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R2, and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning. 相似文献
Journal of Material Cycles and Waste Management - Triboelectric separation is an efficiency and promising method to recycle waste plastics. Fluidized bed has been proved an optimal tribocharger... 相似文献
The Chinese Gridded Industrial Pollutants Emission and Residue Model (ChnGIPERM) was used to investigate potential fractionation effects and atmospheric transport of polychlorinated biphenyls (PCBs) derived from single-source emissions in China. Modeling the indicative PCBs (CB28, CB101, CB153, and CB180) revealed spatiotemporal trends in atmospheric transport, gas/particle partitioning, and primary and secondary fractionation effects. These included the inference that the Westerlies and East Asian monsoons affect atmospheric transport patterns of PCBs by influencing the atmospheric transport time (ATT). In this study, dispersion pathways with long ATTs in winter tended to have short ones in summer and vice versa. The modeled partitioning of PCB congeners between gas and particles was mainly controlled by temperature, which can further influence the ATT. The potential for primary and secondary fractionation was explored by means of numerical simulations with single-source emissions. Within ChnGIPERM, these phenomena were mainly controlled by the temperature and soil organic carbon content. The secondary fractionation of PCBs is a slow process, with model results suggesting a timescale of several decades.
