基于数据包络分析的水污染物排放指标初始分配方法与案例研究

基于数据包络分析(DEA)的点源水污染物排放指标初始分配方法,是将基于DEA的线性规划评价方法进行改进,从而使其适用于评价企业的水环境绩效,并依此进行排污指标初始分配。将非期望产出作为投入指标处理,构造了5输入1输出的规模可变DEA模型,并以南京市280家企业的COD排放指标初始分配为例进行了分析。结果表明,方法的模拟分配结果在公平的基础上,反映了企业的环境效率,效率越高,可得到的COD排放指标也越高,这将对企业治污起到激励作用,分配方法具有一定的可操作性;对案例企业进行分组并实行组内参评,基本可以避免部分极值原始数据对评价结果的影响,得到的COD排放指标分配结果也更为合理。     

高级氧化技术降解环烷酸的研究进展

针对原油及油砂洗脱废水中的环烷酸所具有的酸性、毒性、腐蚀性等特点,介绍了多种降解环烷酸的高级氧化技术,包括Fenton氧化法、臭氧氧化法、光催化氧化法、超临界氧化法、微波辐照法等。评述了高级氧化技术降解环烷酸的最新进展和发现,分析了各种技术的处理效果,并总结了各种技术的优缺点。最后,提出了处理石油行业环烷酸废水的一些思路,为现阶段石油行业环烷酸废水的处理提供了参考。     

蚯蚓生态滤池中蚯蚓与植物对污水净化的作用评估

构建具有不同蚯蚓和植物配置的4个单级蚯蚓生态滤池,依次编号为A(无蚯蚓无植物)、B(有蚯蚓无植物)、C(有蚯蚓栽种芦苇)和D(有蚯蚓栽种水生鸢尾)。通过比较4个滤池在6个月实验期间对化学需氧量(COD)和总氮(TN)的去除效率,系统分析蚯蚓和植物对滤池去除污染物的影响。实验分2个阶段进行:5月上旬至9月上旬,滤池进水碳氮比恒定为6;9月中旬至11月中旬,滤池每周进水碳氮比交替为3、6、9。研究结果表明,蚯蚓对滤池去除COD有一定的促进作用,且作用强度显著受到进水碳氮比的影响。植物的存在与种类对滤池去除COD效率没有显著影响。蚯蚓和植物对滤池的TN去除效率都没有显著影响。滤池进水的碳氮比是显著影响滤池去除TN效率的主要因素。     

脱硫石膏粒径分布与脱水性能实验研究

脱硫石膏粒径分布是影响脱硫石膏浆液脱水性能的重要因素之一,以干法、湿法筛分和研磨破碎获得不同粒径分布的脱硫石膏,在相同的过滤条件下,对不同粒径分布脱硫石膏浆液脱水性能进行了研究,实验结果表明,当脱硫石膏粒径d50大于50μm且（d90-d10）小于100μm,真空过滤最大真空度不低于0．098MPa,过滤时间不少于2．5min,滤饼厚度不超过20mm时,石膏滤饼含水率可以降低到12％以下（最低可达10％）;d50在17μm为脱硫石膏在相同的过滤条件下能够实现真空脱水干燥的转折点;d50和（d90-d10）共同影响石膏脱水性能,d50小于20μm时,仅表现为d50的影响。     

某高拱坝坝基辉绿岩脉改造特征及其工程影响

通过对我国西南某高拱坝坝基花岗岩体内穿插的辉绿岩脉的侵位和后期构造—热液作用以及浅表改造作用的研究,并对比各组不同走向辉绿岩脉的变形破坏特征,查明坝基辉绿岩脉在其活动后期热液蚀变的基础上,在同一构造应力作用下,各组辉绿岩脉具有不同的主要变形破坏方式,即脉壁张裂、剪切滑移和挤压破碎等,从而控制坝基岩体内与岩脉有关的各种软弱结构面、工程岩体结构及其力学性质,并简要讨论了各组岩脉对拱坝坝基抗滑稳定性和变形稳定性的影响。     

三峡库区巴东县龙船河库岸塌岸预测及稳定性评价

采用卡丘金法、工程地质类比法对三峡库区巴东县龙船河库岸不同成因的土质岸坡、岩土混合质岸坡、软硬相间的岩质岸坡在水库蓄水后的塌岸进行了预测,并采用不平衡推力法对其稳定性进行了计算分析与评价,根据预测评价结果进行的防治方案设计,通过了三峡水库三期蓄水的检验,取得了较好的防治效果.     

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。     

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.     

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.