染料废水在旋转式光催化反应器中的降解研究

采用旋转式光催化反应器处理染料废水,探讨染料ｐＨ值,液层厚度,反应器转速,光照时间对脱色效率的影响．结果表明,对所试染料品种,ｐＨ６为最佳值,２０ｍｉｎ为最佳反应时间．液层厚度和反应器旋转速度对半导体光催化氧化反应有一定影响．在优化条件下采用悬浮态ＴｉＯ２时,偶氮染料的脱色率为９８％,采用ＴｉＯ２器壁固定时为７８．５％～９１．５％     

部分水溶性偶氮染料的光催化降解研究

本研究以高压汞灯为光源,系统地研究了１６种偶氮染料在水溶液中的ＴｉＯ２光催化降解。结果表明,这些偶氮染料的光解为一级动力学反应,采用ＴｉＯ２光催化降解方法处理所研究的偶氮染料是可行的,并初步探讨了染料的结构与光解反应表观速率常数之间的关系。     

菌株NKS-3对溴氨酸脱色特性探讨

从受溴氨酸污染地筛选出菌株ＮＫＳ－ ３ ,它对溴氨酸有脱色作用。菌株生长随碳源浓度增加而旺盛,溴氨酸能刺激菌株生长。溴氨酸浓度即使达到１０００ ｍｇ／Ｌ 也能被菌株基本脱色。研究发现,盐度抑制菌生长,在含盐培养液中溴氨酸的脱色过程被抑制     

香港城市固体废物系统管理

本文以大量翔实的数据介绍了香港对城市固体废物的系统管理,即分类、收集、监测分析、处理和预测评价。香港环保署自８０ 年代开始对城市固体废物进行监测并系统管理。在全港设置了３ 个大型策略性填埋场、５ 个垃圾转运站、１ 个垃圾焚烧厂（１９９７ 年５ 月关闭） 和１个化学废物处理中心。目前,香港政府正筹建一个新的、技术先进的焚烧厂。从１９８６ ～１９９７年,这些废物处理设施处理全港废物量为８９６０ ～２４３００ｔ／ｄ,其中城市固体废物比例每年不等,最低为３０ ．７ ％ ,最高为６５ ．５ ％ 。１９９７ 年,这些废物中可循环再利用物接近５０ ％ ,出口部分达１２０ 多万吨,回收资金２０ 多亿港元。根据历年来城市固体废物量与本地生产总值（ＧＤＰ）和人口数量的密切相关性,预测出２０１１ 年城市固体废物人均产率为２ ．５６ｋｇ／ 人·ｄ, 城市固体废物量为１２８１０ ｔ／ｄ。城市固体废物系统管理紧迫而重要,香港的经验是值得我们借鉴的。     

低浓度Pb2+、Cd2+对鲫鱼肝脏组织中 HSP70诱导的影响

以鲫鱼(Carassius auratus)作为实验对象,经过40 d Pb2+、Cd2+不同浓度的暴露后,运用SDS-PAGE和Western Blotting方法检测鱼肝脏组织内应激蛋白HSP70的诱导表达情况.结果表明,在实验浓度下,与对照组相比,Pb2+、Cd2+对鱼肝脏内HSP70有显著的诱导(P＜0.05),但在Cd2+浓度为0.2 mg/L有停止表达HSP70的现象,可能是由于浓度过高造成了组织病理损伤而破坏了诱导表达机制.实验还发现,在实验浓度低于国家渔业用水标准时,HSP70仍然表现为明显诱导(P＜0.05),充分说明运用分子生物学指标要比传统的环境检测指标敏感,具有对污染物早期预警的作用.     

从委托代理理论谈中国的环境影响评价制度

根据委托代理理论,分析了中国环境影响评价制度中存在的问题,立足于绿色招商的理念,理顺建设项目环评委托代理关系,明确了建设单位、环评单位、招商部门、环保部门等相互之间的关系和职责,以使中国的环境影响评价制度更适应新型工业化道路发展的要求。     

Synthesis of branched allylic sulfones by regioselective boron-catalysed hydrosulfonylation

Environmental Chemistry Letters - Branched allylic sulfones are scaffolds widely distributed in bioactive molecules and organic functional materials. The synthesis of allylic sulfones has been...     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

Evaluation of activated sludge properties’ changes in industrial-wastewater pre-treatment: role of residual aluminum hydrolyzed species with different polymerization degree

● Medium poly Al salts dominated the PAC residual salts with a rational dosage. ● Settlement flocculation effect under medium poly Al salts showed a better trend. ● Complex of medium poly Al salts and enzymes promoted cell activity. ● Medium poly Al salts were beneficial to the effluent indexes. With the widespread introduction of pre-coagulation prior to the biological unit in various industrial wastewater treatments, it is noteworthy that long-term accumulation of residual coagulants has certains effect on both micro and macro characteristics of activated sludge (AS). In this study, the morphology distributions of residual aluminum salts (RAS) and their effects on the removal efficiency of AS were investigated under different PAC concentrations. The results showed that the dominance of medium polymeric RAS, formed under an appropriate PAC dose of 20 mg/L enhanced the hydrophobicity, flocculation, and sedimentation performances of AS, as well as the enzymatic activity in cells in the sludge system, improving the main pollutants removal efficiency of the treatment system. Comparatively the species composition with monomer and dimer / high polymer RAS as the overwhelming parts under an over-dosed PAC concentration of 55 mg/L resulted in excessive secretion of EPS with loose flocs structure and conspicuous inhibition of cellular activity, leading to the deterioration of physico-chemical and biological properties of AS. Based on these findings, this study can shed light on the role of the RAS hydrolyzed species distributions, closely relevant to Al dosage, in affecting the comprehensive properties of AS and provide a theoretical reference for coagulants dosage precise control in the pretreatment of industrial wastewater.