Combined remediation of DDT congeners and cadmium in soil by Sphingobacterium sp. D-6 and Sedum alfredii Hance

Combined pollution of 1,1,1-trichloro-2,2-bis (4-chlorophenyl) ethane (DDT) and cadmium (Cd) in agricultural soils is of great concern because they present serious risk to food security and human health.In order to develop a cost-effective and safe method for the removal of DDTs and Cd in soil,combined remediation of DDTs and Cd in soil by Sphingobacterium sp.D-6 and the hyperaccumulator,Sedum alfredii Hance was investigated.After treatment for 210 days,the degradation half-lives of DDTs in soils treated by strain D-6 decreased by 8.1% to 68.0% compared with those in the controls.The inoculation of strain D-6 into soil decreased the uptake of DDTs by pak choi and S.alfredii.The shoots/roots ratios of S.alfredii for the Cd accumulation ranged from 12.32 to 21.75.The Cd concentration in soil decreased to 65.8%-71.8% for S.alfredii treatment and 14.1%-58.2% for S.alfredii and strain D-6 combined treatment,respectively,compared with that in the control.The population size of the DDTs-degrading strain,Simpson index (1/D) and soil respiratory rate decreased in the early stage of treatment and then gradually increased,ultimately recovering to or exceeding the initial level.The results indicated that synchronous incorporation of strain D-6 and S.alfredii into soil was found to significantly (p 0.05) enhance the degradation of DDTs in soil and the hyperaccumulation of Cd in S.alfredii.It was concluded that strain D-6 and S.alfredii could be used successfully to control DDTs and Cd in contaminated soil.     

Mass spectral chemical fingerprints reveal the molecular dependence of exhaust particulate matters on engine speeds

Particulate matters(PMs) emitted by automobile exhaust contribute to a significant fraction of the global PMs. Extractive atmospheric pressure chemical ionization mass spectrometry(EAPCI-MS) was developed to explore the molecular dependence of PMs collected from exhaust gases produced at different vehicle engine speeds. The mass spectral fingerprints of the organic compounds embedded in differentially sized PMs(e.g., 0.22–0.45, 0.45–1.00, 1.00–2.00, 2.00–3.00, 3.00–5.00, and 5.00–10.00 μm) generated at different engine speeds(e.g., 1000, 1500, 2000, 2500, and 3000 r/min) were chemically profiled in the mass range of mass to charge ratio(m/z) 50–800. Organic compounds,including alcohols, aldehydes, and esters, were detected in all the PMs tested, with varied concentration levels for each individual PM sample. At relatively low engine speeds(≤ 1500 r/min), the total amount of organic species embedded in PMs of 0.22–1.00 μm was greater than in PMs of other sizes, while more organic species were found in PMs of5.00–10.00 μm at high engine speeds(≥ 3000 r/min), indicating that the organic compounds distributed in different sizes of PMs strongly correlated with the engine speed. The experimental data showed that the EAPCI-MS technique enables molecular characterization of PMs in exhaust, revealing the chemical dependence of PMs on the engine speeds(i.e., the combustion conditions) of automobiles.     

DDT和三氯杀螨醇对蚯蚓的急性和亚急性毒性影响

采用人工土培养法,通过急性和亚急性暴露实验,研究了DDT和三氯杀螨醇对蚯蚓(赤子爱胜蚓)的存活、生长和繁殖能力的影响.结果表明,DDT在急性暴露期对蚯蚓存活具有一定的毒性,其14 d的LC50为484.76 mg·kg-1;三氯杀螨醇对蚯蚓的存活基本没有影响(LC50＞4000 mg·kg-1).DDT在急性和亚急性暴露期(10～28 d)对蚯蚓的生长都具有显著的抑制作用(p＜0.05),三氯杀螨醇在急性期对蚯蚓的生长没有抑制作用(p＞0.05),在亚急性期对蚯蚓生长的影响尚不确定.DDT和三氯杀螨醇都显著抑制蚯蚓的繁殖能力(p＜0.05).     

PM2.5 concentration estimation using convolutional neural network and gradient boosting machine

Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers (PM_2.5) concentration. Here we explored an image-based methodology with a deep learning approach and machine learning approach to extend the ability on PM_2.5 perception. Using 6976 images combined with daily weather conditions and hourly time data in Shanghai (2016), trained by hourly surface monitoring concentrations, an end-to-end model consisting of convolutional neural network and gradient boosting machine (GBM) was constructed. The mean absolute error, the root-mean-square error and the R-squared for PM_2.5 concentration estimation using our proposed method is 3.56, 10.02, and 0.85 respectively. The transferability analysis showed that networks trained in Shanghai, fine-tuned with only 10% of images in other locations, achieved performances similar to ones from trained on data from target locations themselves. The sensitivity of different regions in the image to PM_2.5 concentration was also quantified through the analysis of feature importance in GBM. All the required inputs in this study are commonly available, which greatly improved the accessibility of PM_2.5 concentration for placed and period with no surface observation. And this study makes an exploratory attempt on pollution monitoring using graph theory and deep learning approach.     

成都市交通运输行业安全生产风险研究

为提升成都市交通运输行业安全生产治理能力和管理水平,本文采用调查研究的方法,从理念认识、体制机制、责任落实和人员素质等方面分析了当前安全生产工作所面临的风险问题,从安全生产综合监管的角色定位入手,从狠抓责任落实、构建双重治理机制、重点领域专项整治和强化基础保障等若干方面,有针对性提出了提升交通运输行业安全生产综合监管能力的对策建议。对交通运输安全研究提供了借鉴和帮助。     

污染场地中有机氯农药对土壤原生动物群的影响

有机氯农药污染土壤对生态环境具有潜在的风险。采集某废弃农药厂污染场地的土壤样品,测定了土壤的理化性质,以及典型有机氯农药滴滴涕(DDTs)和六六六(HCHs)的残留浓度,并对土壤中原生动物的组成和数量进行了分析。结果显示,农药厂区域的土壤中原生动物的组成结构为:鞭毛虫和肉足虫为优势类群,纤毛虫为稀有类群。土壤理化性质与原生动物数量的相关性分析表明,总氮含量与鞭毛虫、纤毛虫数量和原生动物总数量呈显著正相关,总磷含量与肉足虫数量呈显著正相关。同时,农药厂区域土壤中的原生动物数量较对照区显著减少,污染场地中长期残留的有机氯农药对原生动物有明显抑制作用。     

果园土壤有机氯农药残留的时间趋势研究

以天然荒地和蔬菜基地作为对照 ,于 1993年和 2 0 0 3年对北京市某果园中具有不同树龄的老果园和新果园的土壤有机氯农药 (HCH ,DDT)残留情况进行了定位监测 .结果表明 ,在禁用 2 0年后 ,土壤中的有机氯农药残留水平大幅度降低 ,其中老果园的有机氯农药残留浓度显著高于新果园 (P <0 0 5 ) .果园土壤有机氯残留浓度显著高于荒地和蔬菜基地对照 (P <0 0 5 ) .DDT是当地主要的有机氯污染物 ,利用绿色食品生产基地环境质量标准作为评价标准对该果园土壤的污染程度进行了评估 .评估结果表明 ,新果园的土壤质量能够满足绿色食品生产的要求 ,老果园由于土壤中DDT和HCH残留超标严重不适合作为绿色食品生产基地     

内循环生物流化床处理石化废水的中试研究

进行了内循环生物流化床处理石化废水的中试研究,当进水平均ＣＯＤ为８００ｍｇ／Ｌ,流化床出水再辅这以气浮后处理,ＣＯＤ可降至２００ｍｇ／Ｌ,当进水ＣＯＤ为５００ｍｇ／Ｌ时,ＣＯＤ可达１００ｍｇ／Ｌ,本试验中的ＣＯＤ去除容积负荷可达１５ｋｇ／（ｍ＾３·ｄ）以上。     

ODM技术支持下的滑坡位移预测研究

预测模型的建立是滑坡预报的核心问题。Oracle系统的数据挖掘选件(Oracle Data Mining,ODM)可直接对存储在数据库中的数据进行操作,能减少数据转换的资源消耗,保证数据的安全性。以三峡库区八字门滑坡为例,在深入分析滑坡累计位移变化特征与其降雨、库水位等影响因素响应关系的基础上,采用ODM中的PL/SQL API建立了可预测滑坡位移趋势的支持向量机(Support Vector Machines,SVM)模型,并利用建立的SVM模型对滑坡累计位移量的未来值进行了预测。经验证表明:测试样本的预测均方根误差和平均绝对误差分别为10.07mm和6.54mm,预测结果与实测值变化趋势基本一致,说明基于ODM的SVM滑坡位移预测模型在处理非线性时间序列数据上具有一定的优势,可用于滑坡监测数据的短期预测。     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.