河口切变锋引起的滩槽泥沙交换效应

切变锋是河口湾锋系中最为常见和最易观察到的锋面类型。本文对长江口的切变锋进行现场观察和滩，槽同步水文资料分析，简述了长江口切变锋发生的部分和基本特性，提出了切变锋引起的滩，槽泥沙呈现螺旋形交换形式等论点。     

CHemical and Biological Assessment of an Urban, Estuarine marsh in Northeastern New Jersey USA

Oritani Marsh in the Hackensack Meadowlands of urbanized northeastern New Jersey USA was assessed in 2000 for vegetation, soil/sediment chemistry, abundance/diversity of benthic invertebrates, and bird and mammal usage. Vegetatively, both marsh and uplands are dominated by tall, dense Phragmites australis. Small patches (less than 2 hectares total) dominated by Spartina spp. were found at the lowest elevations. Soil/sediment cores were sliced into 5 intervals and analyzed for metals, pesticides and volatile/semivolatile organic compounds. Thirteen locations had at least one chemical above Long et al.’s [Environmental Management, 19, 1995, 81--97] “Effects Range-Median” (ERM). Seven metals and nine organics exceeded ERM in at least one sample, with mercury showing the most exceedances. The surface 15 cm interval was generally more contaminated with metals than the 15 to 30 cm interval; the reverse was true for semivolatile organic compounds. Twenty taxa of benthic macroinvertebrates were collected, with each location producing from 1 to 9 taxa. Abundance ranged from 11 to 3,889 individuals/m². Number of taxa was moderately (r ² between 0.40 and 0.70) negatively correlated with zinc, beryllium, nickel and arsenic concentrations; no other chemical’s r ² was above 0.25. Diversity was moderately negatively correlated with arsenic and beryllium. These correlations were unexpected: zinc, beryllium, nickel and arsenic were not the chemicals found at the highest concentrations relative to benchmarks. Number of taxa, abundance and diversity were moderately (negatively) correlated with elevation; organic carbon was moderately (positively) correlated with abundance. All other correlations were weak (r ² < 0.35). Live traps captured only one mammal species, the meadow jumping mouse. Bird observations revealed 39 species, dominated by a few common species.     

沱江沉积物中有机氯农药分布特征及其与藻类有机质的关系和生态风险评估

使用气相色谱-质谱联用仪分析了长江支流沱江流域48个表层沉积物中有机氯农药（organochlorine pesticides,OCPs）的残留水平,探讨了其分布和组成特征及其与总有机碳（total organic carbon,TOC）、藻类有机质之间的关系,以及评估其生态风险.结果表明沱江流域表层沉积物中OCPs的总含量为3.17~127 ng·g^-1,其中六六六（hexachlorocyclohexane,HCHs）类农药的含量为2.83~86.0 ng·g^-1,滴滴涕（dichlorodiphenyltrichloroethane,DDTs）类农药的含量为0.340~40.9 ng·g^-1.OCPs空间分布特点为：上游 < 中游 < 下游 < 支流.HCHs和DDTs的组成成分分析表明,沱江流域存在林丹输入的现象,主要来自于历史残留,这与绝大多数流域的DDTs的输入状况相似.OCPs含量与TOC、藻类有机质含量之间存在着极显著的正相关性关系,表明藻类有机质在TOC对沉积物中的OCPs分配中起更为重要的作用.生态风险评估表明,沱江流域表层沉积物的有机氯农药存在较大的生态风险,可能对河流的底栖生物及生态环境造成明显的影响.     

富营养化巢湖沉积物溶解性有机质光谱时空分布特征及其环境意义

通过紫外吸收光谱（UV-vis）和三维荧光光谱（3D-EEMs）,研究典型富营养化湖泊巢湖沉积物溶解性有机质（DOM）光谱时空分布特征.结果表明,巢湖表层沉积物DOM的a（254）含量为13.1~101.7 m^-1,平均值为（32.2±16.2）m^-1.其中,夏季沉积物a（254）的含量（（46.6±25.6）m^-1）要显著高于其它季节,且湖心区a（254）平均值和变异性均小于湖滨区.表层沉积物DOM的S_275~295值从春季到冬季上随时间呈明显上升趋势.沉积物DOM荧光强度在夏季最高,在垂直剖面上总体呈下降趋势.DOM组分以酪氨酸类蛋白质和溶解性微生物产物为主,各组分在空间分布上无明显差异.巢湖沉积物DOM荧光指数（FI）为2.56~4.89,腐殖化指数（HIX）为0.57~6.78,生物源指数（BIX）为0.31~1.54.巢湖沉积物DOM主要来自于生物源,藻的生长循环会显著影响富营养化湖泊沉积物DOM的来源和性质.     

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.     

雨水管道沉积物中典型无机氮的干期粒径分布

以南京江北新区分流制雨水管道沉积物为研究对象,考察不同粒径沉积物中铵态氮（NH₃-N）和硝态氮（NO₃^--N）干期分布特征,分析其随干期长度的变化关系,并探讨沉积物理化性质及微生物菌群结构对NH₃-N和NO₃^--N干期分布的影响.结果表明：粒径≤0.075mm的沉积物中NH₃-N占比最高（交通区30.8%,商业区36.7%）;交通区粒径≤0.075mm的沉积物中NO₃^--N占比最高（33.0%）,而商业区粒径0.075~0.15mm沉积物中NO₃^--N占比最高（34.4%）;干期长度与交通区0.075~0.15mm粒径段的沉积物中NO₃^--N含量及商业区粒径≥0.3mm的沉积物中NH₃-N含量之间的相关性均最显著;雨水管道沉积物中NH₃-N和NO₃^--N的干期分布与O-H和N-H等官能团、表面极性和亲水性、微观形貌等有一定关联;交通区雨水管道沉积物中Gemmobacter等反硝化优势菌种（相对丰度总和为20.15%）对NO₃^--N干期分布影响更显著.     

海州湾表层沉积物磷的吸附容量及潜在释放风险

利用磷吸附指数（PSI）、磷吸附饱和度（DPS）和磷释放风险指数（ERI）研究了2016年10月和2017年5月海州湾表层沉积物的磷吸附容量及潜在释放风险.结果显示,2016年秋季PSI变化范围为99.58~199.39[mgP/（100g）]/[μmol/L],DPS变化范围为23.118%~34.289%;2017年夏季PSI变化范围是130.29~198.57[mgP/（100g）]/[μmol/L],DPS变化范围为25.545%~42.135%,两次调查中PSI和DPS均表现出相反的平面分布趋势.PSI和Al_ox、Fe_ox呈显著正相关,说明Fe_ox和Al_ox是影响海州湾表层沉积物吸附磷的主要因素,且Fe_ox占主导作用;DPS与Al_ox和Fe_ox分别表现出了显著负相关性和极显著负相关性,说明Al_ox和Fe_ox含量的增大会降低表层沉积物的磷吸附饱和度.2016年10月磷释放风险指数（ERI）的变化范围为11.59%~34.18%,2017年5月磷释放风险指数（ERI）的变化范围为12.86%~32.34%,从2次调查结果整体来看,海州湾表层沉积物的磷释放风险为中度风险.     

Concentration and Distribution of Persistent Organic Pollutants (POPs) in Harbour Dredging Sediments: A Pilot Study Using a Statistical Approach for a More Efficient Characterisation and Management

The Italian guidelines concerning the dumping of dredgingmaterials implies chemical, physical and microbiologicalcharacterisation of dumping sediments collected through aspecific sampling strategy.POP's such as PCB's and organochlorine pesticides, areconsidered as priority pollutants in all the InternationalConventions for the protection of the marine environment, inconsequence of their persistence, toxicity and bioaccumulation capacity.In the present article the concentrations and distribution ofPCB's and organochlorine pesticides in dredging sediments ofPiombino harbour (northern Thyrrenian sea) were investigates.The results showed high POP's concentrations in all the studiedareas. The maximum values were: 3.94 mg kg^-1 dry weight for PCB's(IUPAC Nos. 28; 52; 101, 118; 137; 153; 180; 209); 2.04 mg kg^-1 d.w.for HCH's (-, -, - and -hexachlorocyclohexane isomers); 0.28 mg kg^-1 d.w. for DD's (DDT + DDE + DDD).Subsequently, the data related to each pollutant were treatedwith statistical tests, in order to verify how well the samplingstrategy is able to represent the distribution of contaminantsin the dredging area. Finally, management strategies werederived for the studied dredging sediments.     

新疆河流洪水水污染分析

洪水期间的水污染主要来源于面源。其特点是污染物的浓度高、负荷量大。给工业、渔业和人民生活都会带来不同程度的危害。悬浮物是洪水过程中的主要面污染产物，同时还作为载体吸附和携带大量的有机物、无机盐、重金属类和微生物进入水体，致使河流水质下降。在研究洪水灾害的同时。不应忽略洪水期间的水污染问题。     

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.