陕西省2000年天气气候异常变化与自然灾害

分析陕西省2000年天气气候变化发现，隆冬降雪明显增多，春旱异常严重，秋霖明显，沙尘暴天气明显增加，这是多年来未曾见到的现象。异常的天气气候使得多种自然灾害并发。     

微量金属元素及其配合物对厨余垃圾甲烷发酵的影响

生物可利用的微量金属元素不仅能够保证污染物以最大的速率转化，而且还可以使某些特殊的转化得以发生，并提高微生物对有毒污染物质的耐受能力。在研究厨余垃圾总固体浓度(total solid, TS)、接种量和C/N比对厨余垃圾厌氧发酵影响的基础上，重点探讨微量金属元素钴及其配合物丝氨酸对厨余垃圾厌氧发酵甲烷产量及关键酶含量的影响。结果表明，当TS为0.5%、接种污泥量为100 mL/L和C/N比为20∶1时，厨余垃圾厌氧发酵的甲烷产率较高，为367 mL/g COD；添加2 μmol/L的微量金属元素钴-配合物丝氨酸时，甲烷产率则提高到432 mL/g COD，相应地，辅酶M的含量由空白实验的41.21 μmol/g VSS提高到54.64 μmol/g VSS，辅酶F₄₂₀的含量由0.31 μmol/g VSS提高到0.48 μmol/g VSS。     

解磷微生物肥料的研究与进展

磷是植物生长必需的矿物质元素之一，在土壤中易被固定降低其有效性，目前，研究和生产含有解磷功能的微生物有机肥料是转化土壤中无效磷、提高磷肥利用率的有效途径之一。叙述了解磷微生物犯料中微生物的种类、生态分布规律和影响高效解磷微生物选育的外部因素，概述了解磷微生物菌肥的应用和研究进展，探讨了解磷微生物肥料的应用价值。为解磷微生物未来的研究重点提出理论依据。．     

垃圾填埋场微生物气溶胶粒径分布研究

为了了解垃圾填埋场微生物气溶胶粒径分布规律,在北京市某垃圾卫生填埋场填埋区、渗滤液处理区、生活区分别选定监测点,利用安德森六级微生物采样器,对填埋场空气微生物进行了系统的定点取样、测定和分析。研究结果表明，空气细菌粒径分布均为第Ⅰ级(>8.2 μm)最高,填埋区空气细菌粒径呈偏态分布,渗滤液处理区、生活区分别在第Ⅳ级和第Ⅲ级出现第2个峰值。携带细菌的可吸入微粒在渗滤液处理区比例最大。空气真菌与放线菌均在第Ⅳ级分布最高,携带真菌和放线菌的可吸入粒子的比例显著大于细菌(P<0.05)。填埋区不同作业时段空气微生物粒径在各级分布比例基本一致。填埋区细菌气溶胶中值直径为5.7 μm,渗滤液处理区为3.7 μm,生活区为5.3 μm，显著大于真菌气溶胶和放线菌气溶胶的中值直径(P<0.05)。     

微波诱导Fe2O3/沸石负载型催化剂催化氧化焦化废水的研究

以天然沸石为载体,采用尿素均匀沉淀法制备了Fe2O3/沸石负载型催化剂(简称负载催化剂),研究其在微波辐射下对焦化废水的处理效果,探讨了负载催化剂投加量、微波辐射功率、微波辐射时间等因素对处理效果的影响,并考察了负载催化刑的重复使用性能.结果表明,天然沸石、于350℃焙烧得到的负载催化刑和于550℃焙烧得到的负载催化剂...     

Sorbent-based devices for the removal of spilled oil from water: a review

Environmental Science and Pollution Research - Always, oil spills do cause serious and dire consequences for the environment, nature, and society that it consumes much time and socio-economic...     

Complexity of the relationship between 2D/3D urban morphology and the land surface temperature: a multiscale perspective

Environmental Science and Pollution Research - Urban morphology is a crucial contributor to urban heat island (UHI) effects. However, few studies have explored the complex effect of 2D/3D urban...     

Status of water use and potential of rainwater harvesting for replacing centralized supply system in remote mountainous areas: a case study

Environmental Science and Pollution Research - The failure of the centralized water supply system forced XY community to become more dependent on uncertain and unstable water sources. The results...     

Microbial reduction of uranium (VI) by Bacillus sp. dwc-2: A macroscopic and spectroscopic study

The microbial reduction of U(VI) by Bacillus sp. dwc-2, isolated from soil in Southwest China, was explored using transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and X-ray absorption near edge spectroscopy (XANES). Our studies indicated that approximately 16.0% of U(VI) at an initial concentration of 100 mg/L uranium nitrate could be reduced by Bacillus sp. dwc-2 at pH 8.2 under anaerobic conditions at room temperature. Additionally, natural organic matter (NOM) played an important role in enhancing the bioreduction of U(VI) by Bacillus sp. dwc-2. XPS results demonstrated that the uranium presented mixed valence states (U(VI) and U(IV)) after bioreduction, which was subsequently confirmed by XANES. Furthermore, the TEM and high resolution transmission electron microscopy (HRTEM) analysis suggested that the reduced uranium was bioaccumulated mainly within the cell and as a crystalline structure on the cell wall. These observations implied that the reduction of uranium may have a significant effect on its fate in the soil environment in which these bacterial strains occur.     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.