预测沉积物-水微宇宙系统中化学品浓度变化的多介质模型

沉积物-水微宇宙系统是经济合作发展组织(Organisation for Economic Co-Operation and Development,OECD)颁布的化学品测试准则中推荐的试验系统之一,可用来测试化学品对底栖生物的慢性毒性。为了在试验前对化学品的浓度变化进行预测,进而确定试验方法,以摇蚊慢性毒性试验系统为例,采用环境多介质模型的建模方法,构建了一种可通过化学品理化性质和试验系统参数,对化学品在沉积物-水试验系统中浓度变化进行预测的模型。结合试验数据和文献资料,给出了模型中试验系统参数的推荐取值,并使用Matlab软件中的Simulink工具对模型进行编程和求解。以此模型为基础,给出了模型在3个方面的应用,即预测蓄积时间、预测平衡时间以及拟合试验数据。对80种已有或假想化学品的蓄积时间和平衡时间进行了计算,得出的范围分别为1~204 d和1~73 d。此外,适当修改模型结构和模型参数,也可将其应用于其他暴露场景中。但使用模型对化学品浓度进行预测时发现,模型仅对沉积物中化学品浓度的预测结果较为准确,而对水中化学品浓度的预测结果与实测值相差1~2个数量级。模型对浓度的预测精度未来仍需进一步提高。上述研究结果完善了沉积物-水微宇宙系统试验方法。     

Diurnal and spatial variations of soil NOx fluxes in the northern steppe of China

NO_x emissions from biogenic sources in soils play a significant role in the gaseous loss of soil nitrogen and consequent changes in tropospheric chemistry. In order to investigate the characteristics of NO_x fluxes and factors influencing these fluxes in degraded sandy grasslands in northern China, diurnal and spatial variations of NO_x fluxes were measured in situ. A dynamic flux chamber method was used at eight sites with various vegetation coverages and soil types in the northern steppe of China in the summer season of 2010. Fluxes of NO_x from soils with plant covers were generally higher than those in the corresponding bare vegetation-free soils, indicating that the canopy plays an important role in the exchange of NO_x between soil and air. The fluxes of NO_x increased in the daytime, and decreased during the nighttime, with peak emissions occurring between 12:00 and 14:00. The results of multiple linear regression analysis indicated that the diurnal variation of NO_x fluxes was positively correlated with soil temperature (P < 0.05) and negatively with soil moisture content (P < 0.05). Based on measurement over a season, the overall variation in NO_x flux was lower than that of soil nitrogen contents, suggesting that the gaseous loss of N fromthe grasslands of northern China was not a significant contributor to the high C/N in the northern steppe of China. The concentration of NO_x emitted from soils in the region did not exceed the 1-hr National Ambient Air Quality Standard (0.25 mg/m³).     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Investigation of physico-chemical properties and microbial community during poultry manure co-composting process

Co-composting of poultry manure and rubber wood sawdust was performed with the ratio of 2:1(V/V) for a period of 60 days. An investigation was carried out to study the extracellular enzymatic activities and structural degradation utilizing Fourier transform infrared spectroscopy(FT-IR), thermogravimetry and differential thermal analysis(TG/DTA)and scanning electron microscopy(SEM). The microbial succession was also determined by using denaturing gel gradient electrophoresis(DGGE). The compost was able to reach its highest temperature of 71°C at day 3 and stabilized between 30 and 40°C for 8 weeks.CMCase, FPase and β-glucosidase acted synergistically in order to degrade the cellulosic substrate. The xylanase activities increased gradually during the composting and reached the peak value of 11.637 U/g on day 35, followed by a sharp decline. Both Li P and Mn P activities reached their peak values on day 35 with 0.431 and 0.132 U/g respectively. The FT-IR spectra revealed an increase in aromaticity and a decrease in aliphatic compounds such as carbohydrates as decomposition proceeded. TGA/DTG data exhibited significant changes in weight loss in compost samples, indicating degradation of organic matter. SEM micrographs showed higher amounts of parenchyma exposed on the surface of rubber wood sawdust at day 60, showing significant degradation. DGGE and 16 S r DNA analyses showed that Burkholderia sp., Pandoraea sp., and Pseudomonas sp. were present throughout the composting process. Ornithinibacillus sp. and Castellaniella ginsengisoli were only found in the initial stage of the composting, while different strains of Burkholderia sp. also occurred in the later stage of composting.     

Pollution levels and characteristics of phthalate esters in indoor air in hospitals

The concentrations of phthalate esters(PAEs) in Chinese hospitals were investigated by simultaneously determining concentrations of gas- and particle-phase PAEs. PAEs were detected in two third-class first-grade hospitals, two second-class first-grade hospitals, and a community health service center. Hospital drugstores had the highest concentration(24.19 μg/m3), which was 1.54 times that of newly decorated houses. The second highest concentration was found in the transfusion rooms, averaging 21.89 μg/m3; this was followed by the concentrations of PAEs in the nurse's workstations, the wards, and the doctor's offices, with mean concentrations of 20.66, 20.0, and 16.92 μg/m3, respectively. The lowest concentrations were found in the hallways(16.30 μg/m3). Of the six different kinds of PAEs found, major pollutants included diethyl phthalates, dibutyl phthalates, butylbenzyl phthalates and di(2-ethylhexyl) phthalates, comprising more than 80% of all PAEs present.Meanwhile, a comparison between different wards showed that PAE concentrations in the maternity wards were 1.63 times higher than in the main wards. Based on known health hazards, our results suggest that the PAEs seriously influence the health of the pregnant women and babies; therefore, it is of great importance to take the phthalate concentrations in hospitals into consideration. In addition, hospital indoor air was more seriously contaminated than the air of newly decorated houses.