QSARs for the toxicity of polychlorinated dibenzofurans through DFT-calculated descriptors of polarizabilities, hyperpolarizabilities and hyper-order electric moments

DFT-B3LYP method with 6-31G(**) basis set was employed to fully optimize the electronic structures of 135 polychlorinated dibenzofurans and parent compound, namely dibenzofuran. It was demonstrated that polarizability anisotropy and mean polarizability could change sensitively and systematically with chlorine number and substitution pattern. And new quantitative structure-activity relationships (QSARs) focused on the binding affinities of aryl hydrocarbon receptor (AhR), aryl hydrocarbon hydroxylase (AHH) and 7-ethoxyresorufin O-deethylase (EROD) induction potencies of PCDFs were developed. It was concluded that polarizability anisotropy in conjunction with hyperpolarizabilties and hyper-order electric moments, e.g. octupole moments could well interpret the variation of toxicity of different congeners and dispersion interaction should be the leading form among various interactions. Although the terms of hyperpolarizabilities and hyper-order electric moments were not the same significant ones as polarizability anisotropy, the long-range interactions characterized by them should not be ignored in explaining the toxicity.     

Effect of amorphous silica and silica sand on removal of chromium(VI) by zero-valent iron

The effect of two surfaces (amorphous silica and silica sand) on the reduction of chromium(VI) by zero-valent iron (Fe(0)) was investigated using batch reactors. The amendment of both surfaces significantly increased the rate and extent of Cr(VI) removal. The rate enhancement by amended surfaces is presumed to result from scavenging of Fe(0)-Cr(VI) reaction products by the provided surfaces, which minimized surface deactivation of Fe(0). The rate enhancing effect was greater for silica compared to sand, and the difference is attributed to silica's higher surface area, greater affinity for reaction products and pH buffering effect. For a given mass of Fe(0), the reactivity and longevity of Fe(0) to treat Cr(VI) increased with increasing dose of silica. Elemental analyses of the reacted iron and silica revealed that chromium removed from the solution was associated with both surfaces, with its mass distribution being approximately 1:1 per mass of iron and silica. The overall result suggests reductive precipitation was a predominant Cr(VI) removal pathway, which involves initial reduction of Cr(VI) to Cr(III), followed by formation of Cr(III)/Fe(III) hydroxides precipitates.     

天津市环境监测元数据模式

元数据技术是目前环境监测信息管理的发展趋势 ,文章对基于元数据的天津市环境监测信息共享模式进行了探索与研究 ,提出了元数据系统功能及内容框架的解决方案。     

固硫生物型煤

研究出生物型煤的生产工艺流程,对型煤和生物型煤的固硫技术进行了研究,成功试验生产出固硫性能优良的生物型煤,为中国煤能源可持续发展利用开辟了新途径。     

山东省主要河流中抗生素污染组成及空间分布特征

采用固相萃取-液相色谱/串联质谱法研究了山东省境内四大流域主要河流中抗生素污染组成及空间分布特征,涉及我国用量最大的6类共20种抗生素。结果表明:20种抗生素均有检出,且大环内酯类、喹诺酮类和四环素类抗生素整体检出浓度较高。就流域而言,半岛诸河流域抗生素污染较小,平均总质量浓度61.4 ng/L;海河流域、小清河流域和淮河流域相对较为严重,平均总质量浓度分别为232、175、118 ng/L。抗生素空间分布呈现一定的规律,检出浓度较高的点位主要集中在人口密集区下游,抗生素污染与周边生活污水、养殖企业废水和城市污水处理厂排水密切相关,而且抗生素组成可从一定程度上反映出污染来源。     

标准物质在环境应急监测定量分析中的作用

以环境空气中挥发性有机物(VOCs)监测为例,比较分析了标准物质在应急监测、实验室定量分析,以及应急监测定量、半定量分析中的作用。结果表明,在有标准物质进行定量校准时,应急监测方法和实验室标准分析方法的定量结果具有可比性;在无标准物质进行半定量分析时,应急监测结果可能存在较大误差。提出,在需准确定量的应急监测中应使用标准物质进行校准,以保证监测结果的准确性。     

高效石油烃降解菌的筛选及其对原油污染土壤的修复

从陕北原油污染土壤中筛选出7株高效石油烃降解菌,其中黄杆菌属CC-2、不动细菌属SC-5、假单胞菌属SC-6表现出较强的石油烃降解能力。通过单因素试验和正交试验考察总石油烃(TPH)降解效果的影响因素,得出各因素对TPH降解率影响程度的大小次序为:溶液p H降解温度降解菌接种量摇床转速,且在降解菌接种量为7%(φ)、溶液p H为7、降解温度为30℃、摇床转速为150 r/min的最适处理条件下,菌株SC-6的TPH降解率可达61.23%。原油污染土壤生物修复实验结果表明:高效石油烃降解菌的投加有利于土壤TPH降解率和酶活性的提高;"菌株SC-6+营养剂"组修复处理42 d后的TPH降解率可达57.59%。     

炼油厂厂界恶臭强度分级评估

石油炼制是产生恶臭污染的重点行业之一,恶臭强度是评估恶臭污染程度的重要参数。以炼油厂厂界大气特征污染物为研究对象,综合考虑检出率、嗅阈值和物质浓度3项指标,采用指标评分法筛选出厂界主要恶臭污染物硫化氢、氨、甲苯和二甲苯。基于韦伯-费希纳定律构建了恶臭强度分级评估方法,以某炼油厂为例进行了厂界恶臭强度评估,结果表明,厂界恶臭强度值为0.63,属于Ⅱ级(轻污染),硫化氢是恶臭强度的主要贡献源。应用感官测定法验证了该评估方法的可靠性。从恶臭管理的角度看,炼油厂在厂界达标排放的基础上应进一步实施恶臭污染综合治理措施,以改善周边的环境质量。     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.