两株重金属抗性细菌对铅镉吸附特性的比较研究

针对目前重金属水污染现状,为探究微生物法修复重金属水污染机制,本试验以抗重金属革兰氏阴性菌Rhizobium sp.W33与革兰氏阳性菌Bacillus sp.H3为供试菌株,对不同重金属(Pb、Cd)浓度及pH条件下的菌株生长状况进行测定;比较不同状态细胞(Growing、Grown、Dead)的吸附能力;利用FTIR初步分析吸附相关官能团.结果表明:两株菌对Pb抗性较强,对Cd抗性相对较弱,最适生长pH均在6~7范围内;不同状态细胞的吸附能力大小为:GrownDeadGrowing;随着重金属浓度的升高,Rhizobium sp.W33与Bacillus sp.H3的吸附容量逐渐增大,且对铅镉的吸附主要以胞外吸附为主,对铅有一定的胞内积累能力;FTIR分析显示菌株吸附过程中主要是—OH、—NH、—CO、—CH2官能团在生物吸附过程中发挥作用.Rhizobium sp.W33与Bacillus sp.H3表面官能团特性及整体吸附能力存在明显差异.     

用加氢工艺生产新型低毒溶剂

针对目前市场上苯类溶剂毒性大，危害人体健康及污染环境，而其他代用品溶解能力差的问题。用加氢工艺开发出了环保型溶剂。其毒性低，溶解性好，使用国产催化剂及原料，有成本低的特点，取得了较好的结果。     

水绵（Spirogyra）对蓝藻复苏及藻类群落结构的影响

通过采集富营养化水体中的底泥与上覆水,在自然光照条件下建立了模拟自然水体的实验装置,并设置了高（42mg.L-1）、低（21mg.L-1）、空白（0mg.L-1）3个水平的水绵添加量,研究了不同剂量的水绵对水华蓝藻（以微囊藻为主）复苏过程及藻类群落结构的影响,同时考察了充足的营养盐在实验中所起的作用.整个实验在藻类的复...     

Comparison of different groundwater vulnerability evaluation models of typical karst areas in north China: a case of Hebi City

Environmental Science and Pollution Research - Groundwater pollution is a serious problem in north China. However, the study on the vulnerability of karst groundwater is mainly in south China, and...     

SDS和PVP强化生物淋溶锰渣的性能

在生物淋溶锰渣的过程中,设置一系列浓度梯度(0～100 mg/L)的离子型表面活性剂SDS(十二烷基硫酸钠)和非离子型表面活性剂PVP(聚乙烯吡咯烷酮),研究两种活性剂对硫杆菌生长和对锰渣中锰的浸出率的影响.结果表明,两种表面活性剂均能有效强化硫杆菌对锰渣的淋溶性能,SDS和PVP的最佳浓度分别为35 mg/L和15 mg/L,在最佳浓度条件下,两者均能显著降低培养液的pH值,提高锰的浸出率,其浸出率分别达到88％和83％,均高于对照组的72％.进一步分析表明,SDS比PVP更能有效提高浸出体系的ORP电位,促进锰的浸出.电镜结果表明,在最佳浓度条件下,SDS比PVP更能有效促进硫杆菌对锰渣表面的侵蚀,降低锰渣的表面粒度,提高锰渣中锰的浸出率.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

Effect of Plasticizer Content on the Structure and Properties of SPI/MA-g-PBAT Blend Films

Journal of Polymers and the Environment - Soy protein isolate (SPI) has the advantages of low cost, easy processing, stable performance and so on. However, the processing temperature of SPI is...