城市生活垃圾堆肥及垃圾专用肥对柑桔增产效应

城市生活垃圾堆肥及垃圾堆肥配制的专用肥对柑桔的肥效实验结果表明，垃圾堆肥作为基肥使用比常规施肥能使柑桔增产１２．４％－１４．５％，比空白对照增产７４．７％，施用垃圾专用肥的柑桔产量比常规施肥增产４．２％－５０．３％，比空白对照增产５８．４％－１２９．０％。施垃圾肥柑桔的营养成份（如维Ｃ，还原糖等）和常规施肥没有明显差异，也未发现重金属对桔子的污染。     

应用立塔式静电除雾器净化宝钢冷轧厂酸洗工艺段酸雾的实践

1998年末 ,鞍山静电技术研究设计院研制的静电除雾器在上海宝山钢铁公司冷轧厂首次应用 ,取得了非常理想的净化效果。本文概述了酸雾对该厂 2 0 30CM0 1酸洗工艺段的危害 ,并对静电除雾器的工作原理、净化过程、设计特点和现场使用等方面作出了系统论述。     

低温下ZCS工艺和改进式ZCS工艺脱氮效率研究及微生物特性分析

考察冬季低温下沸石联合生物吸附再生工艺和改进式沸石联合生物吸附再生工艺对城市污水的脱氮效率,结果表明在HRT为3.5 h下,ZCS工艺出水氨氮为11.65 mg/L,总氮高于20 mg/L.而相同条件下MZCS工艺氨氮出水为5.45 mg/L,总氮去除率比原ZCS工艺提高了11%,均值为16.8 mg/L.通过聚合酶链式技术,变性梯度凝胶电泳和电镜扫描技术发现两工艺微生物多样性、丰度高于传统活性污泥法,兼具活性污泥法和生物膜法的微生物特性.     

循环流化床烟气脱硫技术及其环境经济可行性探讨

简要介绍了循环流化床烟气脱硫技术的原理和国内外研发的现状,并与目前得到广泛应用和继续发展的其它3种烟气脱硫工艺进行比较,得出循环流化床烟气脱硫净化工艺具有投资相对较低、脱硫效率较高、运行可靠、操作维护方便等优点。再通过实例,从投资、技术、运行成本等方面论述其环境经济的可行性,得出循环流化床烟气脱硫技术符合江阴现阶段经济发展状况,能满足中、小型锅炉脱硫要求,是一种较为可行的大气污染控制技术。     

微细水雾除尘技术的实验研究

分析了煤矿并下呼吸性粉尘防治技术现状，并对水的超声雾化技术进行了实验研究，应用回归分析建立了超声雾化性能的数学模型。在分析微细水雾捕尘机理及捕尘水雾凝并沉降技术的基础上，设计了一集微细水雾捕尘-凝聚、凝并降尘-惯性沉降分离为一体的含尘气流净化系统，并对该系统的除尘效率进行了实验研究，建立了相应的数学模型。     

玉米自交系氮效率基因型差异的比较研究

采用溶液培养的方法。选用经田间，土壤试验对N反应有典型差异的玉米自交系478，H21，Wu312,Zong31,Baici，在4个供氮水平下（0.04,0.4,2和4nmol/L)，研究了不同基因型玉米苗期氮素吸收，利用的差异及其原因，结果表明：极低N胁迫条件下（０.04mmol/L),总干重的基因型差异是由根系，地上部的共同作用的结果，高N下，总干重是由地上部的差异所决定的（表1），极低供N条件下，4578的总干重最大，且有较高的氮效率，Wu312,Zong31相对较低，478相对较高的氮效率主要来源于吸收效率的差异。该基因型在低N下总吸氮量最高，H21居中，Baici,Zong31相对较低，478相对较高的氮效率主要来源于吸收效率的差异。该基因型在低N下总吸氮量最高，H21居中，Baici,Zong31,Wu312相对较低（图1），吸收效率的差异主要是由于根系全N量的不同所致（图3），而根系全N量的差异主要是由于根系大小不同（表1），在极低氮水平下，不同基因型玉米根系全氮量的变幅比地上部高出28.1%，高N下（4mmol/L)，全N量的变幅比地上部高出15.4%(图2，图3），在0.04mmol/L下，478与Wu312,Zong31的利用效率不存在显著差异（表2）。     

低磷胁迫下木豆利用磷的基因型差异及其机制

以木豆[Cajanus Cajan(L.)Millsp]MD2、MD5、MD7为材料,采用水培试验方法研究了木豆对磷的吸收、利用的基因型差异及其机理。结果表明:低磷(P1 μmol·L-1)胁迫下,木豆对磷素的吸收和利用效率存在明显的基因型差异,其中MD2、MD7属于磷素吸收、利用效率较强的基因型,而MD5为磷素效率较低的基因型。低磷培养16d后,磷高效的基因型的根/冠比显著增加,根分泌的酸性磷酸酶(APA)活性较高,说明木豆对磷素的吸收效率与植株的根冠比及根分泌的酸性磷酸酶活性有关。在低磷胁迫下,虽然MD7、MD2的含磷量较低,但是磷素利用效率和根尖中APA活性较高,说明根中高APA活性是磷利用效率高的重要原因之一。     

Using Niche-Based Models to Improve the Sampling of Rare Species

Abstract:  Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.     

Efficiency of composite sampling for estimating a lognormal distribution

In many environmental studies measuring the amount of a contaminant in a sampling unit is expensive. In such cases, composite sampling is often used to reduce data collection cost. However, composite sampling is known to be beneficial for estimating the mean of a population, but not necessarily for estimating the variance or other parameters. As some applications, for example, Monte Carlo risk assessment, require an estimate of the entire distribution, and as the lognormal model is commonly used in environmental risk assessment, in this paper we investigate efficiency of composite sampling for estimating a lognormal distribution. In particular, we examine the magnitude of savings in the number of measurements over simple random sampling, and the nature of its dependence on composite size and the parameters of the distribution utilizing simulation and asymptotic calculations.     

Designing an efficient adaptive cluster sample

The choice of neighborhood definition and critical value in adaptive cluster sampling is critical for designing an efficient survey. In designing an efficient adaptive cluster sample one should aim for a small difference between the initial and final sample size, and a small difference between the within-network and population variances. However, the two aims can be at odds with each other because small differences between initial and final sample size usually means small within-network variance. One way to help in designing an efficient survey is to think in terms of small network sizes since the network size is a function of both critical value and neighborhood definition. One should aim for networks that are small enough to ensure the final sample size is not excessively large compared with the initial sample size but large enough to ensure the within-network variance is a reasonable fraction of the population variance. In this study surveys that had networks that were two to four units in size were the most efficient.