常规电除尘器低低温改造分析与研究

低低温电除尘技术可有效提高电除尘器的除尘效率,是常规电除尘器实现"超低排放"的最佳提效改造方式之一。介绍了低低温电除尘技术特点,提出了常规电除尘器进行低低温改造前需要进行的方案选型和技术参数计算,包括酸露点、入口烟气温度、灰硫比、低温腐蚀控制、提效幅度评判。提出了常规电除尘器具体的低低温改造方法,以及低低温改造与"移动电极"或"小分区"技术结合的综合改造技术,并分析了相应的工程改造案例。为常规电除尘器提效改造提供了一种技术方法和应用参考。     

红外分光测油仪测量标准样品石油类不确定度的评定研究

由于测量不确定度便于使用、易于掌握,已被普遍认可作为表征测量结果质量的表达方式。运用红外分光测油仪测量标准样品石油类含量,对所有不确定度分量进行了量化,并找出测量不确定度的来源,从而计算其测量合成相对标准不确定度和扩展不确定度。结果表明：标准样品中石油类的测量结果为20．03 mg／L,扩展不确定度为0．42 mg／L（k＝2）;扩展不确定度贡献较大的主要分量有：加标回收率、样品重复测定和稀释过程引入的标准不确定度分量。     

施用生物质炭对黑土黑碳含量和结构特征的影响

生物质炭(Biochar,Bc)是黑碳(Black Carbon,BC)的一种,当前关于生物质炭在农业上的研究大都集中在其对土壤理化性质和作物生长的影响,而关于施用生物质炭对土壤黑碳结构和数量影响的研究很少.为了解施用生物质炭对土壤黑碳的影响,本试验采用盆栽试验的方法,设置4个处理,施加生物质炭量分别为0(CK)、6、12和24 t·hm-2,采用Wolbach-Anders法对土壤中的黑碳进行提取,运用元素分析、红外光谱和热重分析等方法研究了不同施入量的生物质炭对土壤黑碳产生的影响.实验结果表明:施用玉米秸秆生物质炭能明显增加土壤中黑碳的含量,随着生物质炭施用量的增加,不同处理相对于对照(CK)中土壤黑碳含量分别提升7.04%、22.96%和46.90%.施用玉米秸秆生物质炭降低了黑碳的脂族性、氧化性,提升了黑碳的缩合度、芳化度,提高了黑碳结构的稳定性,提高了土壤黑碳的热稳定性.施用玉米秸秆生物质炭能够提高p H,提高土壤的速效养分含量.     

鲤体内芳香烃受体的分离纯化及其与葸的相互作用

从暴露于污染物蒽的鲤肝脏中提取出芳香烃受体,并采用紫外可见分光光度计进行最大吸收波长扫描,再通过离子交换层析法分离纯化鲤体内的芳香烃受体,并采用聚丙烯酰胺凝胶电泳对受体进行鉴定,最后通过傅里叶变换红外光谱探讨芳香烃受体与蒽结合后的结构变化.结果表明,提取的芳香烃受体在280 nm处有特征吸收峰,测得质量浓度为27.3 mg/mL.通过离子交换层析法纯化了单一组分的受体蛋白质,其相对分子质量约为110 kD.测定了芳香烃受体的红外光谱,与蒽结合后其在一定波段的谱峰发生了明显位移,表明污染物配体蒽能与芳香烃受体结合,并对其蛋白二级结构产生明显影响.     

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation = 3.2, validation r² = 0.91 and RMSE = 13.38 μg g^?1 h^?1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.     

Effects of climatic data low-pass filtering on the ICBM temperature- and moisture-based soil biological activity factors in a cool and humid climate

The air temperature (T_air), total precipitation (TP) and potential evapotranspiration (PET) are standard input data for soil carbon dynamic models, i.e., for calculating temperature and moisture effects on soil biological activity. The resolution needed depends on objectives, the complexity of models and inbuilt pedotransfer functions. The Introductory Carbon Balance Model (ICBM) soil climate front end model calculates a multiplicative soil-temperature (r_{e_temp}) and -moisture (r_{e_wat}) factor with a daily time-step to estimate soil biological activity, i.e., r_{e_crop} = r_{e_temp} × r_{e_wat}. Our objective was to determine how much r_{e_temp}, r_{e_wat} and r_{e_crop} are affected by low-pass filtering of the climatic input data for a cool, humid temperate region. To achieve this we conducted spectral analysis on T_air, TP, PET and r_{e_crop} in the frequency domain. Thereafter we applied Fourier low-pass filters of 5, 15, 30, 60 and 180 days on T_air, TP, PET and tracked their effects through the soil climate model's state variables and outputs. This was done using a sandy and a heavy clay soil and an 89-year daily time-series from a meteorological station in Quebec (Canada). The Fourier spectra showed that the variance for T_air, PET and r_{e_crop} was dominated by an annual cycle, as could be expected. There was no yearly cycle for TP. The variation in r_{e_temp} explained most of the variance in r_{e_crop}. The soil climate module outputs were not sensitive to low-pass filtering of PET. A daily time-step was needed to avoid overestimating r_{e_crop} for the sandy soil. Using a weekly time-step for TP and T_air allowed us to explain about 80% of the variance in r_{e_crop} for the heavy clay soil. This study also indicates that the standard leaf (and green) area index functions for calculating transpiration should receive more attention, since they have significant effects on the state and output variables.     

Evaluation of Bacillus sp. MZS10 for decolorizing Azure B dye and its decolorization mechanism

To evaluate decolorization and detoxification of Azure B dye by a newly isolated Bacillus sp. MZS10 strain, the cultivation medium and decolorization mechanism of the isolate were investigated. The decolorization was discovered to be dependent on cell density of the isolate and reached 93.55%(0.04 g/L) after 14 hr of cultivation in a 5 L stirred-tank fermenter at 2.0 g/L yeast extract and 6.0 g/L soluble starch and a small amount of mineral salts. The decolorization metabolites were identified with ultra performance liquid chromatography-tandem mass spectroscopy(UPLC-MS). A mechanism for decolorization of Azure B was proposed as follows: the C=N in Azure B was initially reduced to –NH by nicotinamide adenine dinucleotide phosphate(NADPH)-dependent quinone dehydrogenase, and then the –NH further combined with –OH derived from glucose to form a stable and colorless compound through a dehydration reaction. The phytotoxicity was evaluated for both Azure B and its related derivatives produced by Bacillus sp. MZS10 decolorization, indicating that the decolorization metabolites were less toxic than original dye. The decolorization efficiency and mechanism shown by Bacillus sp. MZS10 provided insight on its potential application for the bioremediation of the dye Azure B.     

AIR-WATER TEMPERATURE RELATIONSHIP IN ILLINOIS RIVER1

Application of harmonic analysis to daily mean air, water temperature records for a location indicates that the first harmonic accounts for a major portion of the total variance in the records. Water temperature residuals are well correlated with air temperature residuals. Parametric values of the mathematical model for predicting water temperatures from air temperature records are stable from year to year. The air-water temperature relationship appears to be a stationary linear process. Consequently, it is possible to predict water temperatures at a location from the ambient temperature records provided both air and water temperature records are available for another similarly situated water body.     

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.