Copper biosorption on immobilized seaweed biomass： Column breakthrough characteristics

The biosorption of copper by the brown seaweed Sargassum baccularia,immobilized onto polyvinyl alcohol(PVA)gel beads, was investigated with fixed-bed experiments.Laboratory-scale column tests were performed to determine breakthrough curves with varying flow rates and feed concentrations.A theoretical fixed-bed model,known as the Bohart-Adams equation,was evaluated in simulating the experimental breakthrough curves.The Bohart-Adams model qualitatively predicted the breakthrough trends.PVA- immobilized seaweed biomass beads were amenable to efficient regeneration with aqueous solution containing the chelating agent ethylenediaminetetraacetic acid(EDTA).The biosorbent retained most of its original uptake capacity over three cycles of use.The excellent reusability of the biosorbent could lead to the development of a viable metal remediation technology.     

旅游对张家界国家森林公园土壤酶及微生物作用强度的影响

为了探明旅游对张家界国家森林公园土壤生物性状的影响,为保护土壤生态平衡,合理开发生态旅游提供理论依据,进行了旅游人为踩踏对土壤酶及微生物作用强度的影响研究。结果表明:背景区土壤的酶活性>缓冲区土壤的酶活性>活动区土壤的酶活性。背景区土壤的氨化作用强度与微生物活度>缓冲区土壤的氨化作用强度与微生物活度>活动区土壤的氨化作用强度与微生物活度,而硝化作用强度的变化则正相反。旅游显著影响了0～5cm土壤的酶活性及微生物作用强度;在5～15cm土壤和15～25cm土壤层中,背景区和缓冲区的酶活性和微生物作用强度差异不显著,而与活动区的差异显著。有机质分解相关的微生物氨化作用强度、纤维素酶活性、木聚糖酶活性、蛋白酶活性的影响主要是由于土壤有机质的投入减少而造成,而硝化作用强度则主要与踩踏引起的土壤板结造成亚硝酸盐的积累有关,踩踏严重,硝化作用强度大。不同土壤层的微生物作用强度及酶活性强弱是:0～5cm土壤>5～15cm土壤>15～25cm土壤。张家界国家森林公园土壤微生物作用强度及酶活性受到了人为踩踏的破坏,生态系统抵御外界干扰的能力也受到了旅游活动的破坏。     

天然沸石生物再生途径机理研究

在模拟沸石床系统中,对比探讨了曝气、异养菌和硝化细菌3个因素单独或共同作用对沸石再生效果的影响.结果表明,在本试验条件下,曝气作用、异养菌代谢和硝化作用分别可将沸石的再生效率提高0.5%~1.0%、20.9%~31.1%和120%~180%,3个因素影响大小依次为硝化细菌＞异养菌＞曝气吹脱.当异养菌与硝化细菌共存时具有协同再生作用,不仅可提高系统的再生效率(接近100%),而且还可提高沸石的再生率(约10%).离子交换、曝气和异养菌单独或共同作用下沸石的再生过程可用y =1-e-kx方程模拟;存在硝化细菌作用时沸石的再生过程前、后段分别用线性方程y =kx和Monod方程拟合(R2＞0.99).结合沸石再生前后表观形态变化的观测,探讨了沸石的再生机理.     

海泡石颗粒的制备及其吸附油烟效能的研究

用X射线衍射分析了原海泡石矿的纯度,根据原海泡石特性提出“分散-提纯-活化-成型”的制备海泡石颗粒的工艺流程。用所制得的粒径为1.8海泡石颗粒进行油烟吸附实验,分析了海泡石吸附油烟的容量,绘出吸附穿透曲线,并提出海泡石吸附剂再生的方法,同时将海泡石与活性炭吸附油烟的效率做了对比实验。实验结果表明:海泡石成本低,油烟去除率高,是一种理想的油烟净化剂。     

Gaseous mercury fluxes from the forest floor of the Adirondacks

The flux of gaseous elemental mercury (Hg⁰) from the forest floor of the Adirondack Mountains in New York (USA) was measured numerous times throughout 2005 and 2006 using a polycarbonate dynamic flux chamber (DFC). The Hg flux ranged between −2.5 and 27.2 ng m⁻² h⁻¹ and was positively correlated with temperature and solar radiation. The measured Hg emission flux was highest in spring, and summer, and lowest in winter. During leaf-off periods, the Hg emission flux was highly dependent on solar radiation and less dependent on temperature. During leaf-on periods, the Hg emission flux was fairly constant because the forest canopy was shading the forest floor. Two empirical models were developed to estimate yearly Hg⁰ emissions, one for the leaf-off period and one for the leaf-on period. Using the U.S. EPA's CASTNET meteorological data, the cumulative estimated emission flux was approx. 7.0 μg Hg⁰ m⁻² year⁻¹.     

工业污染废润滑油再生技术

润滑油是仅次于燃油的第二大石油制品,机械润滑油在长期使用后将变为废润滑油,成为污染源。对废润滑油的回收再生利用,不仅可以防止废油污染,保护环境,同时可以节约人类有限的石油资源。文章在对当前国内外污染废油再生工艺分析研究的基础上,提出了我国污染废润滑油再生处理关键技术的研究趋势。     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.