城市交通对道路周边土壤重金属污染影响的研究

为研究城市公路交通对路边土壤重金属污染的影响,以北京市西三环主要立交桥路段为研究样区,采用火焰原子吸收光谱法,对土壤中重金属Ni,Cr,Pb,Cu,Zn,Cd的含量进行了测定,并采用改进的BCR连续提取法,对土壤样品中各种重金属的存在形态进行了分析。研究表明:除Cu外,道路周边土壤中其他重金属含量总体上均明显高于同一地区远离交通干道的清洁区土壤;其中Ni,Cr,Pb,Zn,Cd最高分别较清洁区高1.17倍、1.59倍、2.20倍、1.77倍和1.96倍。从各种重金属的形态分布特点看,Cd主要以酸可提取态和氧化物结合态存在;Pb主要以有机物与硫化物结合态存在;Cu,Cr,Zn,Ni主要以残渣态存在。     

高速公路软土地基处理

结合工程实例,提出软土地基处理方案并进行设计计算。以CFG桩为主要处理方法,并进行了各项设计计算与检算。旨在为该软土地区路基设计提供必要的技术分析及加固处理方案。     

短期免耕对黑土孔隙度的影响

对短期玉米免耕和秋翻处理对耕层黑土孔隙度的影响进行了分析。结果表明,耕作处理对土壤孔隙度影响较大,毛管孔隙和贮存孔隙在整个剖面内免耕均小于秋翻,但秋翻处理下的中小孔隙占有很大比例。两个处理之间的大孔隙数量相差很小,多在3%～8%之间。秋翻总孔隙度表现出表层和底层差异很大,而免耕孔隙分布相对均匀,且上下孔隙连通性较好。     

土壤有效磷及其化学测试方法研究进展

磷素对自然系统和农业系统所具有的深远影响仅次于氛而远远高于其他元素。从磷的总体肥力水平上看,磷素缺乏问题是全球性问题。如果土壤全磷含量低作物就极易缺磷,但丰磷土壤容易导致大量的有效磷进入水体造成环境灾难。因此,准确的土壤磷测试方法无论是对农业增产还是对环境保护都具有十分重要的意义。在过去的近百年中,科技工作者在土壤磷素的有效性、磷素的转化、化学测试磷的方法及其作用模式方面做了大量的研究,参35。     

闽江河口湿地土壤甲烷产生潜力动态及对氮输入的响应

采用室内厌氧培养法,对闽江河口芦苇与成草湿地土壤甲烷产生潜力动态及外源氮输入的影响进行了初步研究。结果表明,芦苇湿地与成草湿地土壤甲烷产生潜力最大值均出现在培养后第5d;季节变化的最高值分别出现在9月和12月,最低值均为7月;外源氮输入对成草湿地土壤甲烷产生潜力具有抑制作用,并与对照土壤甲烷产生潜力差异显著（P〈0．05）。图3,参27。     

三江平原典型草甸小叶章湿地土壤的反硝化作用

选择三江平原典型草甸小叶章湿地土壤（草甸沼泽土）为研究对象,采用厌氧淹水培养法,研究了不同土层的反硝化率、反硝化能力及反硝化速率。结果表明,不同土层的反硝化率均随培养时间延长而增加,0～30cm土层的反硝化率较高,30cm以下土层较低且相近;不同土层的氖损失贡献率在不同阶段具有明显的时空分异特征,0～30cm土层的氮损失贡献率较高,介于52．39％和57．91％之间;不同土层的反硝化速率与培养时间均符合一阶指数衰减模型（R2≥0．97）,上层土壤的反硝化速率明显高于下层土壤。不同土层的反硝化率和反硝化速率与土壤理化性质密切相关,反硝化细菌的剖面分布可能对其具有重要影响。图3,表4,参24。     

冯·卡门与卡门涡街

美国宇航局2009年5月公布了自1959年8月14日拍摄第一张卫星照片以来,所选出的50年十佳地球卫星照片,排在十佳照片第一张的是"冯·卡门涡街",从而引起了人们对冯·卡门和卡门涡街的兴趣和关注.     

土壤沉陷特性与车轮滑转关系的离散元研究

 探究轮胎-地面系统的动力学特性,建立更接近轮胎-地面系统实际工作状况的动力学模型是车辆行驶理论的核心问题之一。本研究运用颗粒-车辆地面散体动力学模型,针对目前车辆地面力学中,尚无有效方法对因车轮滑转导致土壤沉陷的问题进行模拟和计算的现状,采取相应的试验数据,建立相同的计算环境,对弹性车轮在沙地上因滑转引起的沙土沉陷问题进行计算,与试验结果进行对比分析表明有很好的一致性。该模型能有效地用于车辆地面力学中滑转率、土壤沉陷特性及两者相互关系的研究和计算。     

Immobilization of arsenic in soils by stabilized nanoscale zero-valent iron, iron sulfide (FeS), and magnetite (Fe3O4) particles

Arsenic is a widespread contaminant in soils and groundwater. While various iron-based materials have been studied for immobilizing arsenic in contaminated soils, the feasibility of stabilized iron-based nanoparticles has not been reported. This study investigates the effectiveness of using three types of starch-stabilized iron-based nanoparticles, including zero-valent iron (ZVI), iron sulfide (FeS), and magnetite (Fe₃O₄), for immobilization of arsenic in two representative As-contaminated soils (an orchard soil and a fire range soil). To test the effect of the nanoparticles on the arsenic leachability, As-contaminated soils were amended with the nanoparticles at various Fe/As molar ratios (5:1–100:1) and contact time (3 and 7 d). After three days’ treatments of a field-contaminated sandy soil, the PBET-based bioaccessibility of As decreased from an initial (71.3±3.1)% (mean±SD) to (30.9±3.2)% with ZVI, (37.6±1.2)% with FeS, and (29.8± 3.1)% with Fe₃O₄ at an Fe/As molar ratio of 100:1. The TCLP-based leachability of arsenic in a spiked fire range soil decreased from an initial (0.51±0.11)% to (0.24±0.03)%, (0.27±0.04)% and (0.17±0.04)% by ZVI, FeS, and Fe₃O₄ nanoparticles, respectively. The Fe₃O₄ nanoparticles appeared to be more effective (5% or more) than other nanoparticles for immobilizing arsenic. When the two soils were compared, the treatment is more effective on the orchard soil that has a lower iron content and higher initial leachability than on the range soil that already has a high iron content. These results suggest that these innocuous iron-based nanoparticles may serve as effective media for immobilization of As in iron-deficient soils, sediments or solid wastes.     

Responses of soil respiration in non-growing seasons to environmental factors in a maize agroecosystem,Northeast China

Based on continuous three-year measurements (from 2004 to 2007) of eddy covariance and related environmental factors, envi-ronmental controls on variation in soil respiration (R_s) during non-growing season were explored in a maize agroecosystem in Northeast China. Our results indicated that during non-growing seasons, daily Rs was 1.08–4.08 g CO2 m^–2 d^–1, and the lowest occurred in late November. The average R_s of non-growing season was 456.06 ± 20.01 g CO2 m^–2, accounting for 11% of the gross primary production (GPP) of the growing season. Additionally, at monthly scale, the lowest value of R_s appeared in January or February. From the beginning to the end of non-growing season, daily R_s tended to decrease first, and then increase to the highest. There was a significant quadratic curve relationship between Rs and soil temperature at 10 cm depth when soil temperature was more than 0°C (P<0.001), with the explaining ratio of 38%–70%. When soil water content was more than 0.1 m³ m^–3, soil moisture at 10 cm depth was significantly parabolically correlated with R_s (P<0.001), explaining the rate of 18%–60%. Based on all the data of soil temperature of more than 0°C, a better model for R_s was established by coupling soil temperature and moisture, which could explain the rate of up to 53%–79%. Meanwhile, the standard error of regression estimation between the values of prediction and observation for R_s could reach 2.7%–11.8%. R_s in non-growing season can account for 22.4% of R_s in growing season, indicating that it plays a critical role in assessing the carbon budget in maize agroecosystem, Northeast China.