Organic residue decomposition: The minicontainer-system a multifunctional tool in decomposition studies

The Minicontainer-test, first described by Eisenbeis (1993), was designed to study the kinetics of organic residue decomposition at a microsite level. It is derived from the litterbag technique and consists of polyethylene minicontainers (volume about 1.5 cm³) filled with a test substrate (litter, straw, cellulose, etc.). The minicontainers (MCs) are closed at either end with plastic gauze discs of variable mesh size (e.g. 20 μm, 250 μm, 500 μm or 2 mm). A definite number of such units are inserted into PVC-bars, which can be implanted into the soil horizontally or vertically, or be exposed on the soil surface horizontally. The bars are very stable and can be exposed in different environments for months to years. If required, the bars can be removed temporarily and stored, e.g. during soil cultivation. Should fresh litter be used, two phases of decomposition can be distinguished: a fast initial phase, which can be mainly related to the effect of leaching, and a second slow phase depending mainly on the activity of soil organisms and litter quality. Several questions can be addressed to investigations using MCs, e.g. 1) parts of the soil fauna which are involved in decomposition (nematodes, microarthropods, and smaller specimens of the macrofauna, e.g. enchytraeidae, diplopods and dipteran larvae) can be extracted from the litter substrate using a miniscale high gradient extractor, 2) the organic mass loss of litter can be determined, 3) microbial biomass (C_mic, N_mic) can be assessed by fumigation extraction and 4) microbial activity (respiration) in the test substrate can also be assessed by use of standardised methods. Compared to litterbag studies, the larger number of small replicate units improves the statistical evaluation. Until today the Minicontainer-test has been applied in forestry and agriculture, e.g. studying the effects of liming, soil restoration and the application of insecticides, e.g. Diflubenzuron (Dimilin) and Btk (Bacillus thuringiensis var.kurstaki).     

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。     

混合堆肥过程中挥发性固体含量的层次效应及动态变化

城市污泥与猪粪混合堆肥表明:升温期和高温期堆体上部有机物降解的差异较大,降温期堆体下部各层有机物降解的差异较大,且堆体内挥发性固体(VS)含量已形成明显的层次效应,腐熟期堆体中部有机物降解的差异较大,堆体内VS含量的层次效应仍很明显;不同堆肥期堆体内VS含量的差异由大到小分别为:降温期≥腐熟期>高温期>升温期,不同部位有机物降解程度由大到小为:上部>中部>下部;VS含量随时间的变化满足一级反应动力学方程,城市污泥和猪粪混合堆肥过程中VS的降解速率约为0.3kg·d^-1,VS含量降低了8.2%.     

水溶性有机物对土壤中菲的生态毒性影响

采用生物培养和物理化学试验,研究了水溶性有机物(DOM)对土壤中phe生态毒性的影响.结果表明,本研究所用DOM均具有表面活性;小麦根phe毒害的敏感区间为0～200mg/kg,200mg/kg是土壤中phe的50%小麦根伸长抑制率浓度.DOM的存在会降低phe的生态毒性,但是降低程度与其疏水性馏分含量和表面活性有关,且phe生态毒性的降低程度随DOM浓度的增大而增大.本研究的结果说明DOM能减轻土壤中phe的生态毒性.     

活性污泥数学模型中异养菌产率系数的测定

活性污泥数学模型常被用于预测污水处理厂中的生物过程。模型中异养菌产率系数YH对其他组分的测定值有显著的影响。目前．测定YH的主要方法有序批式活性污泥法和序批式呼吸计量法。在序批式呼吸计量法中．起始F／M值的选择十分关键。以醋酸钠为底物．对这两种方法进行了分析比较,序批式呼吸计量法为文章的推荐方法。     

不同共代谢基质下四氯乙烯厌氧生物降解研究

分别用葡萄糖、乳酸盐和醋酸盐作为驯化好的厌氧污泥的共代谢基质 ,对四氯乙烯 ( PCE)的降解进行研究。结果表明 ,PCE是通过还原脱氯发生生物降解的。实验的回归结果表明 ,反应均符合一级动力学方程 ;反应速率常数的大小依次为 k乳酸盐 >k葡萄糖 >k醋酸盐 ;以乳酸盐作为共代谢基质时 ,PCE的降解速率较快 ,在实验条件下乳酸盐是最合适的共代谢基质     

污水回用中的循环浓缩水质模型与污水回用率

污水回用中因循环浓缩,存在着水质变差问题．探索其变化规律,定量确定运行参数,做到既节水又保证回用水质合格、稳定,具有重要理论和实用意义．通过数学归纳法推导出一个循环浓缩水质数学模型和计算杂用水质和污水回用率的公式．应用该模型,以厕所污水回用为例,就污水水质、污染物去除率、处理水质和杂用水质标准对杂用水质、污水回用率的影响进行讨论．结果表明,在符合城市杂用水水质标准的前提条件下,分别按生化需氧量、浊度、氨氮及色度等水质指标计算得出的污水回用率是65．3％,82．9％,57．5％和37％．在厕所污水回用中,色度为水回用率的限制指标．由于色度指标的限制,经计算水循环厕所的污水回用率一般不宜超过40％。     

电解对氯苯酚稀水溶液中脱氯降解机理研究

利用石墨电极进行了多种条件下对氯苯酚水溶液的电解研究。研究结果表明,CODCr去除率和脱氯率随电压的升高呈先升高后下降的趋势、随对氯苯酚质量浓度的升高呈指数下降、随溶液pH的升高而升高。碱性条件下电解效果明显优于中性和酸性条件。在电压为10V,pH≈12,电解100mg L的对氯苯酚溶液2h后,CODCr去除率可达52 94%,脱氯率达52 8%。由脉冲辐解瞬态吸收光谱可知,中性、酸性条件下降解机制均为OH·自由基的作用,经过瞬态分子OH-adducts(OH加合物)的中间产物进一步氧化降解;碱性条件下通过OH·自由基和对氯苯酚氧基负离子的阳极直接失电子氧化作用2种降解机制,经过瞬态分子氯代酚氧自由基中间产物氧化降解对氯苯酚,产物分析结果显示生成了对苯二酚和苯醌等中间产物。      

大型仓库防火分区问题性能化设计研究

本文运用性能化设计方法对某大型仓库防火分区问题进行分析说明,其结果勿需对空间进行实体分隔,从而实现动态分区。为了实现了动态分区,通过采用合理的消防对策和减少火灾时热释放速率的方法,防止火灾扩散和蔓延。本文运用ISO9705实验装置研究了该仓库内主要可燃物(轮胎)在不同堆放方式下的热释放速率,通过确定可燃物堆放方式来减少该仓库的热释放速率,由于研究结果表明了可燃物水平堆放比垂直堆放的热释放速率小得多,从而可燃物应水平堆放;同时本文也对采用标准喷头和快速响应早期抑制喷头的自动喷水灭火系统分别进行了科学计算,比较二者的结果所得：标准喷头不能实现对着火源的控制,而快速响应早期抑制喷头可以对着火源的控制,该仓库的自动喷水灭火系统采用快速响应早期抑制喷头可以将火势控制在一定的范围内,火源上方(z=6．8m)温度分布也表明该仓库不需要进行钢结构保护。     

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.