太湖北部小流域沉积物重金属污染特征与评价

近几十年来重金属的沉积记录、来源及污染水平受到了广泛关注.为探明太湖北部小流域重金属的污染来源和环境质量状况,本研究分析了柱状沉积物中8种重金属（Mn、Cr、Zn、Cu、As、Pb、Ni和Co）含量.运用主成分分析法判定污染源,富集因子法表征重金属富集特征,通过熵权模糊模型对沉积物中重金属污染状况进行评价.结果显示,所有重金属含量均较高,高低依次为Mn > Cr > Zn > Cu > As > Pb > Ni > Co.其中Mn含量最高,为380.06~767.69μg/g.沉积物重金属污染的主要贡献因子为交通排放、农业污染和煤炭燃烧.As的富集指数最大（2.89~15.61）.其中77.59%的沉积层属于显著富集,受人类活动影响最为显著.熵权模糊综合评价结果表明,Cu（0.483）和As（0.352）的权重值最大,是沉积物中的首要污染物,且65.52%的沉积层重金属含量处于中度污染水平.     

汶川地震重灾区泥石流灾损土地利用及生态修复模式——以北川县都坝河小流域为例

汶川地震诱发了大量泥石流灾害,灾损土地利用和生态修复是灾区产业重建面临的重要课题。以北川县都坝河小流域为研究对象,通过调查灾损土地禀赋、灾害特征、土地需求,采取多因素耦合和关键因子限制分析法,探讨灾害胁迫条件下的经济活动与生态修复之间的互馈作用,结果表明：（1）流域新增土地供给源主要为泥石流灾损土地,土地资源化利用受灾害、聚落和产业结构控制;（2）灾损土地根据成因划分为沟口堆积型、沟道冲淤型以及岸坡侵蚀型,三者的肥力、安全性及交通条件等特征具有显著差异;（3）基于“因灾分区、耕地优先、产业共建、美居造景”的原则,建立了灾损土地的利用方式和生态修复模式,并选取杨家沟进行验证,沟域灾损土地利用方式为生态林地、产业林地及优质耕地,分别占比28.5%、56.3%、15.2%,生态修复措施主要为提高土地安全度、提升植被覆盖率和增强水保功能。该研究建立的震区土地利用和生态修复模式可有力协调人地矛盾、发展绿色经济和提升人居环境。     

Heterogeneity in the relationship between carbon emission performance and urbanization: evidence from China

Mitigation and Adaptation Strategies for Global Change - Global change caused by carbon emissions alone has become a common challenge for all countries. However, current debates about urbanization...     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

WK－1A型二氧化硫自动测定仪测定烟气中SO_2的方法研究

本文研究了仪器电导法测定烟气中ＳＯ２的监测方法，并与碘量法进行了比较，提出了吸收液测定的影响及吸收液的配制方法。本方法简单快速，能够满足烟气中ＳＯ２监测工作的要求。     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

镉─邻菲啰啉配合物吸附波的研究与应用

在ｐＨ３．３的盐酸－醋酸钠介质中．镉－邻菲啉配合物在－０．６５Ｖ（ｖＳ．ＳＣＥ）处产生一灵敏的吸附波，其二次导数波高与镉浓度在０．０００６～０．１ｍｇ／Ｌ范围内呈良好的线性关系，检出下限达０．０００３ｍｇ／Ｌ。极谱波性质的研究表明该波具有配合物吸附波的特征。方法已用于环境水样中微量错的测定，结果与其它方法吻合。     

水环境数学模型在河流水质管理规划中的应用

本文讨论了水环境数学模型的选择及在水质管理规划中的使用方法,达到实用的目的.具有广泛的通用性和实用性.