长江三峡地区坡地发育初步研究

坡地发育是地貌学研究的基本问题之一。三峡地区坡地发育的研究对三峡库岸稳定性的评估和三峡移民的土地开发具有十分重要的意义。首先从概括坡地发育的研究进展，阐述科学家们对坡地发育的不同看法入手，引出三峡地区坡地发育的特殊性。初步研究三峡地区的坡地发育具有分段式分进的特点，包括中山峰陡坡的剥蚀后退过程，山顶缓坡的风化剥蚀夷平的过程，上段斜坡的片流坡地侵蚀过程，下段陡坡的剥坡后退过程，山麓堆积缓坡的剥蚀过程等。通过分析三峡地区坡地的五种坡段的成因、现状、发展趋势以及它们对三峡库区的稳定性和三峡移民的土地开发的影响，预测未来的三峡库岸，大部分为剥蚀陡坡，部分为崩塌滑坡陡坡，三峡沿岸城镇开发建设的重点将是库岸有坡地的防护工程。     

Study on the Balance of Agricultural Water and Land Resources on Ningxia Plain

The article puts forward the process and means of regional water and land balance research, and then from two scenarios which are the balances under natural regulation and human intervention, calculated and analysed the balance between water and land on Ningxia Plain. For the balance under natural regulation named farmland water balance, using farmland water resource balance equation, the research estimated the monthly farmland＂ water balance of 8 major crops for all of the 12 counties on Ningxia Plain in the period of 1960-2001; for the balance under human intervention, the research estimated land-use water balance equation of the counties in 2000, and calculated the balance between land use and water resources including irrigating water of all the 12 counties on Ningxia Plain. Results showed that ①precipitation can not meet the water demand of the crops for growth and development on Ningxia Plain, and water shortage is the primary character of farmland water balance under natural regulation. ②the diversity of water and land balance of different counties is distinctly influenced by the crop structure, water quantity for irrigation and irrigation level. ③Irrigation water could meet the crop water demand on Ningxia Plain in 2000, but there was not much space to expand irrigating cultivated land.     

城市灾害相对承载力分析与模型的建立

如何评价城市系统综合承灾能力,这一直足被普遍关注也是较难解决的问题.在综合型可持续发展度评价模型的基础上建立的城市系统灾害相对承载力评价模型,为解决上述问题提供了新的思路.通过对城市复杂功能系统中社会子系统、经济子系统和环境子系统的划分及其相互影响分析,找出各子系统中对承灾能力的重要影响因素,进行加权整合后分别得出社会安全指数、经济"软"指数、环境指数和基础设施指数,应用这些承灾能力指数建立了灾害相对承载力模型.建模计算过程中发现因素量化方式的选取和权重的确定是比较重要的,会影响灾害相对承载力的合理取值,因此以经济"软"指数为例,提出了用协调度系数乘积代替原计算式值以使其取值范围在合理区间内的解决方式.     

超临界CO2回收线路板工艺研究

采用正交试验设计进行了超临界CO₂回收废弃印刷线路板的实验。以温度、压强、时间和夹带剂为实验因素，通过对实验前后样本在重量、厚度、弯曲强度和断裂强度四方面变化的分析考察了各实验因素对实验效果的影响。实验结果表明，影响超临界CO₂法回收印刷线路板的最主要因素为温度、时间和夹带剂，超临界CO₂法回收废弃印刷线路板的最佳工艺条件：温度270℃，压强大于7.38 MPa，时间3 h，夹带剂(水)160 mL。实验进一步验证了压强对实验结果的非显著性影响。     

TiO2-xNx光催化氧化苯甲酸的研究

采用悬浮态光催化反应体系，以氙灯为光源，自制纳米TiO2-xNx为催化剂，考察了苯甲酸光催化氧化过程中TiO2-xNx质量浓度、反应体系初始pH和苯甲酸初始质量浓度对苯甲酸溶液中TOC去除率的影响。实验结果表明：最佳催化剂质量浓度为0．30g／L；TiO2-xNx的等电点为pH=5．3，在酸性条件下（pH=2．0）更有利于苯甲酸在催化剂表面的吸附，以及光生电子与O2反应生成·OH的进行；随苯甲酸初始质量浓度增加，TOC去除率降低，反应速率也相应地降低。TiO2-xNx比P25型TiO2表现出更强的浓度适应能力。     

Ti/MnO2/PbO2电极的制备及电催化降解罗丹明B

采用电沉积法制备了Ti／MnO2／PbO2电极，并用其对质量浓度为20mg／L的罗丹明B模拟废水进行了电催化降解实验。实验结果表明：原水pH为4．95，无须调节废水的pH；最佳电解实验条件为：支持电解质NazSO4浓度0．09mol／L，电解电流密度40mA／cm^2，处理时间40min，在此条件下罗丹明B的去除率接近100％。     

A directional passive air sampler for monitoring polycyclic aromatic hydrocarbons (PAHs) in air mass

A passive air sampler was developed for collecting polycyclic aromatic hydrocarbons (PAHs) in air mass from various directions. The airflow velocity within the sampler was assessed for its responses to ambient wind speed and direction. The sampler was examined for trapped particles, evaluated quantitatively for influence of airflow velocity and temperature on PAH uptake, examined for PAH uptake kinetics, calibrated against active sampling, and finally tested in the field. The airflow volume passing the sampler was linearly proportional to ambient wind speed and sensitive to wind direction. The uptake rate for an individual PAH was a function of airflow velocity, temperature and the octanol-air partitioning coefficient of the PAH. For all PAHs with more than two rings, the passive sampler operated in a linear uptake phase for three weeks. Different PAH concentrations were obtained in air masses from different directions in the field test.     

应用陶粒过滤-陶瓷膜组合对止咳糖浆制药废水深度处理的试验研究

止咳精浆制药废水经生物接触氧化一体化废水处理装置处理后,其出水中COD、BOD和氨氮等主要污染物质均不能达标排放.在实验室中采用陶粒过滤-陶瓷膜组合对经一体化处理装置处理后的制药废水进行深度处理,在陶粒柱反应器选用粒径为1～2 mm的陶粒滤料,陶瓷膜反应器选用孔径为2～3/μm陶瓷膜的条件下对水样进行测试.废水经陶粒过滤-陶瓷膜组合处理后,其BOD、COD、SS和氨氮指标均可稳定达标排放.     

Pattern Classification and Recognition of Invertebrate Functional Groups Using Self-Organizing Neural Networks

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.