Evaluation of the gene expression changes in Nile tilapia (<Emphasis Type="Italic">Oreochromis niloticus</Emphasis>) as affected by the bio-removal of toxic textile dyes from aqueous solution in small-scale bioreactor

A laboratory-scale bioremediation unit was designed, built and tested for the bio-removal of several Direct textile dyes. Four experiments were carried out to assess the efficiency of the bioremediation unit using Aspergillus niger fungal strain. Three commonly used Direct dyes and textile dyes mixture (simulated effluent: Direct brown, Direct violet, Direct green) were tested in this study. The strain of A. niger was efficient in the removal of the three Direct dyes. The decolorization percentages of the dyes after 24 h of incubation were 56.2, 51.7, and 95.4% for Direct brown, Direct green, Direct violet dyes, respectively. The percentages increased up to 79.4, 86.4, and 96.7% after 72 h of incubation for the same dyes, respectively. The results also showed that the fungal strain reduced the chemical oxygen demand values of simulated dye effluents from 165 to 564 mg/l with most of the dyes. The assessment of bioremediation products on biomodel was conducted using a fresh water fish. The liver and brain of Nile tilapia were tested to evaluate the expression of genes coding for several proteins related to stress such as metallothioneins (MTs), cytochrome P450 (CYP450), and heat shock proteins (HSPs). To assess the alterations in the gene expression, ten animals from each group were killed after 4 weeks of treatment. The results revealed significant increases in the brain and hepatic mRNA levels of all stress protein genes MT, CYP450, Hsp70a, b, and Hsp47 in the fish groups treated with industrial Direct violet, green, and brown dye water. Exposure of tilapia to bioremediation products after treatment with A. niger fungi reduced the over-expression of the stress protein genes in the brain and liver tissues.     

京、津、冀地区地闪特征的统计分析

京、津、冀地区地闪的区域特征十分明显,利用该地区2004年的地闪的定位资料,通过分析得到以下主要结论:(1)正地闪占总闪的比率为10.86%,而各地区正地闪占总地闪的百分率与相应地闪产生地区的中心纬度之间都存在着很好的线性关系.由于与中、低纬度地区相比,高纬度地区雷暴云整体高度发展得较低,雷暴云中主正电荷区域的位置也较低,因此高纬度地区较中、低纬度地区的正地闪发生得多.(2)雷暴以7月份为最多,且7月份地闪次数的逐日分布较均匀.(3)地闪在空间分布上有4个相对较为集中的区域,这些区域主要是山地的迎风坡、或下垫面水汽的充足处、或人口相对集中的城市中心区.     

An Interval-Parameter Waste-Load-Allocation Model for River Water Quality Management Under Uncertainty

A simulation-based interval quadratic waste load allocation (IQWLA) model was developed for supporting river water quality management. A multi-segment simulation model was developed to generate water-quality transformation matrices and vectors under steady-state river flow conditions. The established matrices and vectors were then used to establish the water-quality constraints that were included in a water quality management model. Uncertainties associated with water quality parameters, cost functions, and environmental guidelines were described as intervals. The cost functions of wastewater treatment units were expressed in quadratic forms. A water-quality planning problem in the Changsha section of Xiangjiang River in China was used as a study case to demonstrate applicability of the proposed method. The study results demonstrated that IQWLA model could effectively communicate the interval-format uncertainties into optimization process, and generate inexact solutions that contain a spectrum of potential wastewater treatment options. Decision alternatives can be generated by adjusting different combinations of the decision variables within their solution intervals. The results are valuable for supporting local decision makers in generating cost-effective water quality management strategies.     

间歇运行式生物滴滤池处理油漆废气中试研究

为研究间歇运行式生物滴滤池对油漆生产厂废气净化能力，建立一座中试规模生物滴滤池（BTF），接种降解菌群，采用8 h/d运行方式净化某油漆厂包装车间废气，并用PCR-DGGE技术揭示BTF细菌群落结构与工艺运行条件间的联系。油漆厂包装车间废气中挥发性有机物（VOCs）主要为甲苯、乙苯、混合二甲苯（间、对和邻二甲苯），BTF对甲苯、乙苯、混合二甲苯最大去除率分别为88.8%、83.7%和86.3%。DGGE分析显示，BTF稳定运行时，主要优势菌相对丰度较为稳定（F，P>0.05），其细菌多样性显著低于启动期（F，P>0.05）；同时，下层填料细菌多样性高于上层填料，其细菌结构变化也较上层明显；另外，提升培养液浓度至2倍和4倍对菌群结构亦无显著影响。     

Exporting natural capital: the foreign eco-footprint on Costa Rica and implications for sustainability

As the world economy ‘globalizes’, trade has become a major mechanism by which much of the human population supports its needs. While trade in resource commodities (natural income) can increase the well-being of people in both exporting and importing countries, it can also lead to depletion of natural capital and the loss of ecosystems integrity. In recent years, various researchers have attempted to address this problem using a consumption-based perspective on ecological change. Their work shows that the loss of ecosystem integrity in almost any region of the world can be attributed to both local and international consumer demand. This paper illustrates the utility of modified eco-footprint analysis in assessing export-related ecological change in Costa Rica. We quantify ecological footprint of consumers around the world on the productive ecosystems of Costa Rica, document the changing character of this footprint and highlight some of the linkages between production for export in Costa Rica and ecological degradation. We then discuss the implications of the increasing trade-based entanglement of nations for ecosystems and global sustainability.     

Social and ecological facets of pest management in Honduran subsistence agriculture: implications for IPM extension and natural resource management

In subsistence farming systems of the developing world, adoption of resource-conserving practices such as integrated pest management (IPM) is often strikingly low. This has partially been ascribed to researchers’ limited understanding of how technologies develop at the interface of the systems’ social and ecological components. In Honduras (Central America), there exists concern about limited adoption and diffusion of IPM technologies in certain smallholder production systems. In this study, we determine social and ecological drivers of IPM adoption in subsistence maize production in the country’s hillside environment. Honduran small-scale maize production is typified by a key insect pest (the fall armyworm, Spodoptera frugiperda) being partly kept at bay through action of a diverse natural enemy complex, including ants, social wasps, carabid beetles, and spiders. Local agricultural landscapes, primarily shaped through shifting cultivation, provide key resources to maintain this natural enemy diversity. These local ecological conditions and related natural enemy abundance strongly influence farmers’ agroecological knowledge and pest management practices. In the meantime, farmer practices are also affected by local communication networks, which help validate and spread IPM concepts and technologies. Based on our findings, we advocate a holistic approach to improve IPM extension through mapping of agroecological opportunities, visualization of regional patterns in farmer knowledge, and associated priority setting. Local IPM capacity could be built through institutional strengthening and adaptive comanagement, while IPM training should be linked with natural resource management initiatives. These approaches may eventually improve the way IPM is delivered to small-scale farmers who operate in the ecologically diverse environments of the tropics.     

Prediction of membrane fouling using artificial neural networks for wastewater treated by membrane bioreactor technologies: bottlenecks and possibilities

Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane resistance to filtration, such as the wastewater characteristics, the mixed liquor constituents, or the operational conditions, for example. Worldwide applications of MBRs in wastewater treatment plants treating all kinds of influents require new methods to predict membrane fouling and thus optimize operating MBRs. That is why new models capable of simulating membrane fouling phenomenon were progressively developed, using mainly a mathematical or numerical approach. Faced with the limits of such models, artificial neural networks (ANNs) were progressively considered to predict membrane fouling in MBRs and showed great potential. This review summarizes fouling control methods used in MBRs and models built in order to predict membrane fouling. A critical study of the application of ANNs in the prediction of membrane fouling in MBRs was carried out with the aim of presenting the bottlenecks associated with this method and the possibilities for further investigation on the subject.     

Methane biofiltration in the presence of ethanol vapor under steady and transient state conditions: an experimental study

Methane (CH₄) removal in the presence of ethanol vapors was performed by a stone-based bed and a hybrid packing biofilter in parallel. In the absence of ethanol, a methane removal efficiency of 55 ± 1% was obtained for both biofilters under similar CH₄ inlet load (IL) of 13 ± 0.5 g_CH4 m^?3 h^?1 and an empty bed residence time (EBRT) of 6 min. The results proved the key role of the bottom section in both biofilters for simultaneous removal of CH₄ and ethanol. Ethanol vapor was completely eliminated in the bottom sections for an ethanol IL variation between 1 and 11 g_ethanol m^?3 h^?1. Ethanol absorption and accumulation in the biofilm phase as well as ethanol conversion to CO₂ contributed to ethanol removal efficiency of 100%. In the presence of ethanol vapor, CO₂ productions in the bottom section increased almost fourfold in both biofilters. The ethanol concentration in the leachate of the biofilter exceeding 2200 g_ethanol m^?3 _leachate in both biofilters demonstrated the excess accumulation of ethanol in the biofilm phase. The biofilters responded quickly to an ethanol shock load followed by a starvation with 20% decrease of their performance. The return to normal operations in both biofilters after the transient conditions took less than 5 days. Unlike the hybrid packing biofilter, excess pressure drop (up to 1.9 cmH₂O m^?1) was an important concern for the stone bed biofilter. The biomass accumulation in the bottom section of the stone bed biofilter contributed to 80% of the total pressure drop. However, the 14-day starvation reduced the pressure drop to 0.25 cmH₂O m^?1.     

Model and algorithm for bi-fuel vehicle routing problem to reduce GHG emissions

Because of the harmful effects of greenhouse gas (GHG) emitted by petroleum-based fuels, the adoption of alternative green fuels such as biodiesel and compressed natural gas (CNG) is an inevitable trend in the transportation sector. However, the transition to alternative fuel vehicle (AFV) fleets is not easy and, particularly at the beginning of the transition period, drivers may be forced to travel long distances to reach alternative fueling stations (AFSs). In this paper, the utilization of bi-fuel vehicles is proposed as an operational approach. We present a mathematical model to address vehicle routing problem (VRP) with bi-fuel vehicles and show that the utilization of bi-fuel vehicles can lead to a significant reduction in GHG emissions. Moreover, a simulated annealing algorithm is adopted to solve large instances of this problem. The performance of the proposed algorithm is evaluated on some random instances.