武汉城市圈土地利用变化系统仿真模拟与驱动力分析

土地利用变化是全球环境变化与可持续研究的重要内容,驱动力研究是土地利用变化研究中的核心问题。现有土地利用变化驱动力研究多针对单一的城市区域展开,无法解释城市群地区,城市一体化战略指导下城市交互作用对土地利用变化的影响。运用系统动力学(SD)方法,综合考虑经济、人口、城市化、政策等主要人文因素,并将城市交互作用因素引入SD,以武汉城市圈为例,构建土地利用变化系统SD模型,运用1997~2006年的历史数据进行检验,并对武汉城市圈2010~2020年不同情景下的土地利用变化进行模拟。结果表明,建立的SD模型是有效的,两种情景下土地利用变化表现出差异性,说明城市交互作用是除经济、人口城市化等社会经济因素外,土地利用变化的又一重要影响因素。     

鄱阳湖流域排污权初始分配模式的比较研究

排污权初始分配是排污权交易的起点和基础,在分配模式和实证研究方面需要加强。采用地区人口法、经济总量法、历史排污量法、环境容量法和综合分配法5种模式研究了鄱阳湖流域11个地市水污染物化学需氧量(COD)与氨氮的初始排污权分配。结果表明:COD和氨氮初始排污权在地市之间的分配格局相似;南昌、新余与鹰潭3市按经济总量模式分配的排污量明显高于其它模式,上饶、吉安两市人口分配模式高于其它模式,景德镇、九江两市历史排污量模式高于其它模式,赣州市环境容量模式高于其它模式,宜春市综合模式高于其它模式。综合分配模式为赣州市排污权总量最高,南昌、九江、吉安、宜春、抚州和上饶6市次之,景德镇、萍乡、新余、鹰潭4市最低。总体来看,排污权配额因分配模式不同而有所差异,综合分配模式得到的初始排污权最为合理。建议鄱阳湖流域严格执行综合分配模式下的各地市初始排污权分配额度,确保区域经济社会与生态环境的协调发展。     

挥发性有机污染物排放控制标准制订中的关键技术问题研究

研究分析了挥发性有机污染物排放标准制订中4个关键技术问题:污染因子选择、控制指标确定、限值的确定以及技术管理措施.结果表明,应根据毒性和光化学反应性选择污染因子;确定控制指标应既考虑有组织控制指标,又考虑无组织的控制指标;确定排放限值应明确是基于控制技术还是基于健康风险;同时制定技术管理措施以通过源头和过程控制挥发性有机污染物排放.     

典型喀斯特小流域水文水化学过程对旱季暴雨的响应

通过定位监测和样品跟踪采集,在旱季对喀斯特小流域连续20天无前期降雨的一场暴雨(累计降雨量64mm)水文水化学过程进行了动态监测,并分析了相关的影响因素。结果表明,小降雨事件(8.5mm)未能引起溪流水位、电导率及温度的明显变化,而当累计降雨量达到35.5mm后溪流水开始响应。此后,溪流水文水化学参数对降雨快速响应,除硝态氮外,电导率(EC)、δ18 O、Ca2+迅速降至最低值,雨水稀释作用对其水化学变化起主要作用。通过对溪流新旧水比例进行划分发现,降雨前期和后期,溪流以旧水补给为主,而在水位快速上涨阶段,新水比例达到34%左右。两次降雨过程中,在溪流水水位升高的情况下,水体中硝态氮浓度仍然表现出高于雨前浓度的趋势。该研究为该区旱季小流域水资源利用和污染物防治方面提供了一定理论依据。     

Interspecific and intraspecific identification of Dendrobium based on the psbAtrnH intergenic region sequences and the 5S rRNA gene spacer sequences北大核心CSCD

This research aimed to investigate the interspecific and intraspecific identification of Dendrobium by using the multi-locus method so as to provide a molecular basis for Dendrobium identification through the combination of chloroplast psbA-trnH intergenic region sequences and ribosome 5S rRNA gene spacer sequences. PCR direct sequencing was applied to detect the chloroplast psbA-trnH intergenic region sequences as well as the ribosome 5S rRNA gene spacer sequences of 12 Dendrobium species, while the psbA-trnH intergenic region sequences of Dendrobium denneanum dq-2 variety and dq- 5line were cloned and sequenced for single nucleotide polymorphism (SNP) analyzing. The sequences were analyzed by the software Sequencher4.14, Bioedit7.0, MEGA5.2 and Dansp5.0; the interspecific and intraspecific Kimara-2-Parameter(K2P) distances were also calculated. The phylogenetic tree (using Neighbor joining method) was constructed with Bulbophyllum odoratissimum and Bletilla striata as outgroup. The results showed an average length of chloroplast psbA-trnH gene sequences in Dendrobium as 742.3 bp, with 72 variable sites, including 33 information sites; the average length of the ribosome 5S rRNA gene spacer sequences in Dendrobium was 336.4 bp, with 213 variable sites including 139 information sites. Using psbAtrnH intergenic region sequences in combination with ribosome 5S rRNA gene spacer sequences can not only identify D. denneanum, D. hancockil, D. thysiflorum, D. devonianum, D. moniliforme, D. chrysotoxum, D. officinale, D. heterocarpum and D. nobile, but also differentiate D. officinale from different geographical populations, and distinguish the dq-2 variety and dq 5line with SNP in the multi locus of D. denneanum.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

涪陵页岩气田钻井岩屑资源化利用实践

总结了涪陵页岩气田开发初期钻井岩屑采用固化填埋方式处置存在占地面积大且易产生二次污染隐患等问题,因地制宜地探索钻井岩屑的资源化利用。分析了水基岩屑随钻预处理和油基岩屑热脱附技术,结合钻井岩屑的理化性质,开展了水基岩屑脱水后干渣制路基材料、制砖及水泥窑协同处置,油基岩屑灰渣制混凝土、制砖及水泥窑协同处置的探索和实践,基于环境安全、技术稳定可靠、消纳量大的原则,最终形成了涪陵页岩气田钻井岩屑资源化方案:水基岩屑"随钻预处理、水泥窑协同处置",油基岩屑"热脱附、水泥窑协同处置",实现了钻井岩屑的资源化综合利用。     

微波水热法制备ZnO-还原氧化石墨烯纳米复合材料及其光催化性能

通过快速沉淀-NaBH_4微波水热还原制备了ZnO-还原氧化石墨烯(RGO)纳米复合物.采用X射线衍射(XRD)、激光拉曼光谱(Raman)、透射电子显微镜(TEM)、X射线光电子能谱(XPS)和光致发光(PL)等测试手段对复合光催化剂进行表征.结果表明,复合材料中的氧化锌为六方晶系纤锌矿结构,并均匀覆盖在RGO表面上,其直径大约为15—20 nm.ZnO-RGO复合材料的光催化性能明显优于氧化锌,为纯ZnO的2.5倍.光催化性能提高可能归因于RGO优良的电子传输能力加速了ZnO-RGO纳米材料光生载流子的分离效率.