利用X射线衍射和岩矿鉴定等技术研究河南汤家坪钼矿区主要矿物标型特征

汤家坪钼矿床是2006年探明的一处大型斑岩型钼矿床,前人用常规地质方法对该矿区基础地质工作和成矿规律研究已比较系统和完整,认为汤家坪钼矿床属于深源浅成斑岩型钼矿床,汤家坪花岗斑岩体为矿区钼矿的成矿母岩;但是没有从矿物学角度来应证这些成矿规律。本文从矿物学角度,采用分离单矿物进行化学分析、X射线衍射(XRD)及岩矿鉴定等技术,对汤家坪钼矿区的辉钼矿、黄铁矿、石英、长石4种矿物标型特征(化学组成、微量元素、晶体结构、晶胞参数、结构状态等)进行研究。结果表明:1该矿区辉钼矿全部是2H型多型变体,且成分中Re含量很低;辉钼矿的δ34S值为3.0‰,硫同位素变化范围小,接近于陨石硫,具深源硫的特点,硫来源于花岗斑岩。2黄铁矿微量元素以富含Mo、Co、Cu,贫Ni、Pb、Zn为标型特征,另外Au、Ag含量低,S/Se大于250000,与前人"S/Se大于250000~500000的地区不可能找到金矿"的结论相符。3石英富含Mo;成矿期石英脉中δ18O降低,表明成矿晚期有少量大气降水参与热液蚀变成矿。4长石富含Mo;轻稀土(La~Nd)含量大于中稀土(Sm~Ho)和重稀土(Er~Y)含量;钾长石的有序度、三斜度以及结构参数的值均较高,结构温度较低,表明该矿区钾长石为低温状态下的三斜对称的微斜长石,是热液作用的产物,岩体钾长石化对钼矿化最为有利。本研究应证了汤家坪钼矿床属于深源浅成斑岩型钼矿床,对于总结斑岩型钼矿成矿规律、找矿标志有重要意义。     

锰钾矿光催化降解苯酚及其影响因素研究

利用回流法合成隧道结构的锰钾矿,并研究其光化学降解苯酚的效果及影响因素。分别采用X射线衍射、原子吸收光谱、扫描电镜、透射电镜和BET氮气吸附法对锰钾矿的晶体结构(包括晶型、晶胞参数和结晶度)、化学组成、微观形貌和比表面积进行了表征。研究表明,非光照时锰钾矿对苯酚的降解效果较差,光照能显著促进锰钾矿对苯酚的降解;光照条件下,p H值减小能显著促进锰钾矿对苯酚的降解;锰钾矿用量增加至1.00 g/L时能显著促进锰钾矿降解苯酚,但继续增加矿物用量却显著降低苯酚降解率;非光照条件下,p H值减小和矿物用量增加不能显著促进锰钾矿降解苯酚。     

硫代硫酸钠沉淀法富集铜冶炼废酸中铼的实验研究

白银有色集团铜业公司冶炼废酸量每年约为1×105m3,其中铼含量为5~70 mg/L,为综合利用废酸中的铼,采用硫代硫酸钠作为特效沉淀剂,对该沉淀剂加入量、反应时间、反应温度以及废酸中铼含量对铼沉淀率的影响做了较系统的实验研究,得到较优化的沉淀条件。进一步的中试放大实验取得了良好的技术指标:富铼渣泥中含铼达1.66%,铼的回收率达到99.8%。在此基础上,对沉淀剂的氧化还原反应机理进行了探讨。本方法工艺简单,铼沉淀率高,环境友好,较适用于废酸中铼的高效提取。     

粉细砂地层防渗帷幕综合施工方法

四川省武都水库右岸EL574 m灌浆平洞揭露出厚度2.3～9.8 m的粉细砂层,普通水泥高压灌浆无法形成可靠的防渗帷幕。采用高压旋喷、湿磨细水泥、化学灌浆的组合处理方式,取得了较好的施工效果。通过钻孔压水、大功率声波CT、单孔声波、钻孔全景数字成像等方法检测,表明达到了设计防渗要求。     

基于形态学分析铅锌矿不同功能区土壤重金属元素的分布特征及污染评价

为掌握尤溪铅锌矿区土壤重金属元素分布特征和污染现状,通过采集尤溪铅锌矿不同功能区0~20cm表层土壤样品,测定土壤中Pb、Zn、Cd、Cu、Cr 5种重金属元素的总量及其化学形态,分析不同功能区土壤重金属污染及分布特征,同时采用次生相与原生相比值法(RSP)进行污染评价。结果表明,尤溪铅锌矿不同功能区土壤中Pb、Zn、Cd含量均超过国家土壤环境质量三级标准,废弃冶炼区土壤Cd、Cu、Pb、Zn含量均最高,分别为标准值的14.78、1.13、3.73、1.34倍,采矿区Cr含量最高,但未超过标准限值。重金属形态结果表明,相比其他元素,Cd弱酸提取态所占比例最高,Cu可交换态比例最高,Pb、Zn、Cr以残渣态为主。RSP法评价表明,不同重金属的污染程度表现为:Cu(1.15)Cd(0.80)Pb(0.59)Zn(0.57)Cr(0.54);不同功能区土壤重金属污染表现为:尾矿库区冶炼区废弃冶炼区采矿区。SPEF法评价表明,尤溪铅锌矿区受人为污染明显,采矿区和尾矿库区Zn污染最为严重,冶炼区和废弃冶炼区Pb污染最为严重,功能区污染顺序为:尾矿库区冶炼区废弃冶炼区采矿区。     

Spatiotemporal patterns of tropical deforestation and forest degradation in response to the operation of the Tucuruí hydroelectric dam in the Amazon basin

The planned construction of hundreds of hydroelectric dams in the Amazon basin has the potential to provide invaluable ‘clean’ energy resources for aiding in securing future regional energy needs and continued economic growth. These mega-structures, however, directly and indirectly interfere with natural ecosystem dynamics, and can cause noticeable tree loss. To improve our understanding of how hydroelectric dams affect the surrounding spatiotemporal patterns of forest disturbances, this case study integrated remote sensing spectral mixture analysis, GIS proximity analysis and statistical hypothesis testing to extract and evaluate spatially-explicit patterns of deforestation (clearing of entire forest patch) and forest degradation (reduced tree density) in the 80,000 km² neighborhoods of the Brazil's Tucuruí Dam, the first large-scale hydroelectric project in the Amazon region, over a period of 25 years from 1988 to 2013. Results show that the average rates of deforestation were consistent during the first three time periods 1988–1995 (620 km² per year), 1995–2001 (591 km² per year), and 2001–2008 (660 km² per year). However, such rate dramatically fell to half of historical levels after 2008, possibly reflecting the 2008 global economic crisis and enforcement of the Brazilian Law of Environmental Crimes. The rate of forest degradation was relatively stable from 1988 to 2013 and, on average, was 17.8% of the rate of deforestation. Deforestation and forest degradation were found to follow similar spatial patterns across the dam neighborhoods, upstream reaches or downstream reaches at the distances of 5 km–80 km, suggesting that small and large-scale forest disturbances may have been influencing each other in the vicinity of the dam. We further found that the neighborhoods of the Tucuruí Dam and the upstream region experienced similar degrees of canopy loss. Such loss was mainly attributed to the fast expansion of the Tucuruí town, and the intensive logging activities alongside major roads in the upstream reservoir region. In contrast, a significantly lower level of forest disturbance was discovered in the downstream region.     

中国南方湿润区“荒漠化”问题讨论

荒漠化是当今世界所面临的严重的环境与社会经济问题,推进荒漠化研究工作对于保护生态环境,促进区域可持续发展具有重要意义。本文在对荒漠化内涵进行系统回顾的基础上,认为除了人为干扰叠加气候性干旱作用导致的荒漠化之外,南方湿润区因水土流失叠加物理性干旱作用导致的极端土地退化也是一种荒漠化,并从物质基础、气候条件、影响因子、形成营力、核心作用和地表过程方面进行比较,最后以南方红层区特殊的"红层荒漠化"为案例,利用实地调查和TM影像的植被覆盖度指标VFC对1992-2013年红层土地退化进行分析。结果显示,在人为反复干扰和其他自然因素作用下地表植被持续退化、群落干旱趋势明显,表土层流失,岩石风化加剧,以致形成类似"荒漠"景观的现象。红层区的退化过程可类比于喀斯特石漠化、红壤山地丘陵退化,但这类极端土地退化现象可否视为"荒漠化",有待于进一步的深入研究。     

Land desertification and restoration in Middle East and North Africa (MENA) region

The Middle East and North Africa (MENA) region is characterized by high population growth, degraded and fragile nat-ural ecosystems, and a limited amount of arable lands. It is one of the most water-sc...     

石灰土中秸秆还田降解率及其对土壤肥力的影响

石灰土具有高的有机碳的积累,但营养成分的供给速率偏低,导致土壤贫瘠。本文通过室内模拟实验,研究相同来源玉米秸秆在岩溶土与非岩溶土中的降解情况,对秸秆降解率、土壤理化性质、土壤肥力等进行动态检测,结果表明：（1）总体上岩溶土秸秆降解速率比非岩溶土快。秸秆降解速度最快主要集中在最初60天,之后呈缓慢上升趋势,到98天基本达到平衡。非岩溶土降解峰值出现时间为第42天,岩溶土为第28天,峰值出现时间前者比后者滞后14天。总降解时间持续160天,岩溶土降解率为77%,非岩溶土为75%。（2）秸秆降解期间,土壤pH值前期有下降的趋势,后期慢慢回升,这与秸秆降解过程中不同时期降解产物变化规律相吻合。秸秆还田可以提高土壤肥力,其中速效氮和速效钾含量增加非常明显。（3）与岩溶土相比,全量检测结果显示各肥力指标在非岩溶土含量低,但升幅百分比高;有效态检测结果显示非岩溶土速效磷和速效钾含量高。说明秸秆还田一些营养元素有效态在非岩溶土中更易于释放,而岩溶土由于其土壤质地的特殊性,许多养分被土壤粘土矿物牢固结合,营养元素供给速率慢,但因此也易于养分全量的累积。     

Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data — where do the severely degraded areas occur?

The Tibetan Plateau in Western China is the world’s largest alpine landscape, sheltering a rich diversity of native flora and fauna. In the past few decades, the Tibetan Plateau was found to suffer from grassland degradation processes. Grassland degradation is assumed to not only endanger biodiversity but also to increase the risk for natural hazards in other parts of the country which are ecologically and hydrologically connected to the area. However, the mechanisms behind the degradation processes remain poorly understood due to scarce baseline data and insufficient scientific research.We argue that remote sensing data can help to better understand degradation processes and patterns by: (1) identifying the distribution of severely degraded areas and (2) comparing the patterns of key spatial attributes of the identified areas (altitude above sea level, aspect, slope, administrative districts) with existing theories on degradation drivers. Therefore, we applied four Landsat 8 images covering large portions of the three counties Jigzhi, Baima and Darlag in the Eastern Tibetan Plateau. The dates of the Landsat scenes were selected to cover differing phenological stages of the ecosystem. Reference data were collected with a remotely piloted aircraft and a standard consumer RGB camera. To exploit the phenological information in the Landsat data as well as deal with the problem of cloud cover in multiple images, we developed a straightforward PCA-based procedure to merge the Landsat scenes. The merged Landsat data served as input to a supervised support vector machine classification which was validated with an iterative bootstrap procedure and an additional independent validation set. The considered classes were “high-cover grassland”, “grassland (including several stages of grassland vitality)”, “(severely) degraded grassland”, “green shrubland”, “grey shrubland”, “urban areas” and “water bodies”. Kappa accuracies ranged between 0.84 and 0.93 in the iterative procedure, while the independent validation led to a kappa accuracy of 0.76. Mean producer’s and user’s accuracies for all classes were higher than 80%, and confusion mainly occurred between the two shrubland classes and between the three grassland classes.Analysis of the slope, aspect and altitude values of the vegetation classes revealed that the degraded areas mostly occurred at the higher altitudes of the study area (4300–4600 m), with no strong connection to any specific slope or aspect. High-cover grassland was mostly located on sunny slopes at lower altitudes (less than 4300 m), while shrubland preferred shady, relatively steep slopes across all altitudes. These observations proved to be stable across the examined counties, while the proportions of land-cover classes differed between the examined regions. Most counties showed 5–7% severely degraded land cover. Darlag, the county located at the edge of the permafrost zone, and featuring the highest average altitude and lowest annual temperature and precipitation, was found to suffer from larger areas of severe degradation (14%).Therefore, our findings support a strong connection between degradation patterns and climatic as well as altitudinal gradients, with an increased degradation risk for high altitude areas and areas in colder and drier climatic zones. This is relevant information for pastoral management to avoid further degradation of high altitude pastures.