港湾海洋环境监测站位布设方法研究——以象山港为例

本文在水质污染物扩散模拟的基础上结合港湾生物、水质、水团、水动力等分区特征,确定港湾监测代表单元,结合质心点计算方法,建立港湾海洋环境监测站位优化布局方法,并在象山港进行示范。结果表明:(1)象山港作为典型半封闭港湾,水交换能力较差,水团分布相对均匀,根据无机氮、活性磷酸盐水质模拟结果及港湾生态分布特征,象山港分成9个区,各区内主要水质指标及生物生态特征基本一致。(2)采用质心点计算方法,在象山港9个区及区间(分界线)共设置16个代表性站位,各站位可作为象山港落潮时无机氮、活性磷酸盐、温、盐、生物等指标的监测站位;(3)站位优化验证结果表明,优化前后2组数据之间无显著性差异,说明优化后的16个监测站位能较好地代表象山港海域的生态环境质量,其所表征的监测信息量与现有监测站位的信息量等效。     

基于GIS的海水环境监测站位优化——以胶州湾为例

文章利用2003-2012年5月、8月和10月胶州湾19项海水监测要素归一化后数据,基于ArcGIS软件地学统计模块,通过Kriging插值模型对归一化数据进行检验分析及站位优化。结果表明,优化后的31个监测站位估计误差标准差梯度较优化前大幅降低,平均估计误差标准差由0.160降低到0.147,降低了8.3%。站位优化后不仅可对胶州湾沿岸重点污染源以及胶州湾海域总体海水质量实施有效监控,并且可以作为今后胶州湾海水监测的常规站位。优化后的监测网在满足监测精度需要的同时,能够较大程度节省监测网的运行费用,可以为其他海域海水监测站位优化及海洋主管部门提供技术支撑,具有重要的应用价值。     

基于ＧＩＳ的海水环境监测站位优化

文章利用２００３—２０１２年５月、８月和１０月胶州湾１９项海水监测要素归一化后数据,基于ＡｒｃＧＩＳ软件地学统计模块,通过Ｋｒｉｇｉｎｇ插值模型对归一化数据进行检验分析及站位优化。结果表明,优化后的３１个监测站位估计误差标准差梯度较优化前大幅降低,平均估计误差标准差由０．１６０降低到０．１４７,降低了８．３％。站位优化后不仅可对胶州湾沿岸重点污染源以及胶州湾海域总体海水质量实施有效监控,并且可以作为今后胶州湾海水监测的常规站位。优化后的监测网在满足监测精度需要的同时,能够较大程度节省监测网的运行费用,可以为其他海域海水监测站位优化及海洋主管部门提供技术支撑,具有重要的应用价值。     

象山港海域环境容量及其分配研究

文章根据象山港污染源和海域环境容量的确定,以行政区划为计划单元,结合当地的社会经济发展现状及规划,对象山港周边5个县(市、区)海域的环境容量进行优化分配,使象山港海域污染物总量控制研究更具可操作性;同时对象山港海域污染物总量控制工程进行了积极探讨,并提出了相应的对策与建议。     

渤海水质监测站位优化研究

以2000年-2010年无机氮、活性磷酸盐和化学需氧量这三个主要污染因子趋势性监测数据为基础,采用水质空间稳定性评价、监测站位时空重要性评价以及站位网络优化设计等技术方法,对渤海2010年原有的海洋环境趋势性水质监测站位进行优化调整,为渤海海域水质的时空变化趋势评价,提供更为有效、客观的支撑。     

象山港海域分区评价方法研究

文章根据2011-2018年在象山港海域调查的象山港水文、水团、水环境、沉积物环境、生物生态等调查数据，将象山港海域划分为若干个具有不同生态变化特征的区域，研究象山港海域内的空间异质性，以便剖析各区域环境的问题。研究发现：象山港水交换特征、水团分布、水环境、沉积环境和生物生态特征将象山港划分为A、B、C、D、E 5个区评价较为合理。该方法可以有效并合理地对象山港海域做出评价，并为相关海洋科学工作者和管理者提供科学依据。     

象山港大型底栖动物群落特征

2006年7月—2008年8月,对象山港全港海域设立的13个采样站位采集的调查资料,用物种丰富度指数、物种均匀度指数和物种多样性指数分析象山港大型底栖动物物种的多样性和群落种类的组成,以及采用ABC曲线方法和大型多元统计分析软件PRIMER5对象山港大型底栖动物进行Bray-Curtis相似性聚类分析和非度量MDS标序,研究象山港大型底栖动物群落结构。调查显示象山港大型底栖动物不同采样站位之间物种丰富度指数(d)、均匀度指数(J)、辛普森多样性指数(D)和香农-威纳多样性指数(H’)差异皆高度显著(P0.01)。群落结构聚类分析和MDS标序表明,13个采样站位的大型底栖动物群落大致可分为3组。根据所调查象山港大型底栖动物的丰度和生物量资料做的ABC曲线分析表明,该海域大型底栖动物群落受到了严重的环境污染或者扰动,逐渐由一种或几种个体较小的种类占优势。     

基于地理坐标的海洋环境监测站位代码设计

为解决目前我国海洋环境监测站位编码不统一、不唯一和不稳定等问题,提高监测数据应用水平和海洋信息化能力,文章参考我国渔区编码规则,基于海洋环境监测站位的地理坐标设计站位代码,并以山东省海域为例具体说明。海洋环境监测站位代码由监测区、监测小区和监测点3个部分组成8位阿拉伯数字,将海域按经、纬度各30′划分监测区并顺序编码0000~9999(第1~4位),将每个监测区按经、纬度各3′划分监测小区并顺序编码00~99(第5~6位),将每个监测小区按经、纬度各0.3′划分监测点并顺序编码00~99(第7~8位)。该代码设计规则仅与监测站位的地理坐标相关,具有统一性、唯一性和稳定性,且容量较大,可满足我国海洋环境趋势性监测和绝大部分海洋环境业务化监测的需要。     

象山港海水养殖及其对环境的影响

利用2000年及2002年对象山港海域海水养殖现状及环境监测的调查和分析资料,对海水养殖业的发展给象山港周边生态环境带来的影响进行了分析,结果表明,2000年象山港海域水体中无机氮含量超四类海水水质标准的测站占86.7%,2002年已达到了100%,因而该海域水体全年均呈富营养化状态,水质主要污染因子为氮和磷,海水养殖给环境带来了一定的影响与污染。防治该海域环境污染应采取调整饵料结构、推广生态养殖、实行科学管理等有效的措施。     

象山港养殖海域水体和沉积物中营养元素的分布特征及其控制过程的初步研究

利用2005年5月到9月对象山港海域五个航次的监测结果,对象山港海域水体和底质中营养元素的分布特征及其控制因素进行了初步的探讨。结果表明,象山港海区水体和沉积物中营养元素含量较高,水质恶化严重;海产养殖是造成本海区营养元素超标的主要因素,也是控制营养元素在空间和时间上变化的主要因素。     

Monitoring meiobenthos using cm-, m- and km-scales in the Southern Bight of the North Sea

Meiobenthos at the mouth of the Grevelingen and Oosterschelde Estuaries in the Southern Bight of the North Sea was monitored using meiobenthic abundance and nematode community structure. Hierarchical sampling included 54 subsamples nested within 18 cores within six stations within two transects. The spatial patterns of the meiofaunal populations and communities are presented in cm-scale (among subsamples), m-scale (among cores), km-scale (among stations) and 10 km-scale (between transects). The variance components analysis of meiofaunal abundances showed that km-scale and 10 km-scale represented 63–90% of total variance while m-scale and cm-scale provided only 10–37%. Different communities were found among stations rather than among cores or subsamples by detrended correspondence analysis ordination and clustering analysis. The main assemblage of meiofauna occurred at the km-scale. Nematode diversity was more powerful than meiofaunal abundance in detecting spatial variation at all scales. However, at least two replicates are required for monitoring when sampling using a 10 cm² subsampling core.     

海洋仪器测试海区综合环境信息监测系统的研发

提出了一种基于海洋仪器测试海区所开发的环境信息监测系统设计方法，通过无线网络获取海区内台站、浮标等观测单元所采集的环境要素数据，实现数据的存储、查询和管理。长时间测试表明，该系统工作稳定，可为仪器设备的海上测试与评价提供基础实测数据。     

据赤道经向风剖面及岛屿站风资料诊断厄尔尼诺事件

据赤道经向风剖面及热带西太平洋岛屿站测风资料来诊断厄尔尼诺（ＥｌＮｉｎｏ）事件,试图从观测事实方面来跟踪与预测ＥｌＮｉｎｏ的发生。分析结果得出：气候监测公报中８５０ｈＰａ风指数对监测、诊断已发生的ＥｌＮｉｎｏ事件较好,但不能用来预测ＥｌＮｉｎｏ事件的发生,而热带西太平洋岛屿站月平均纬向风对ＥｌＮｉｎｏ事件的发生有一定的指示意义。从越赤道气流的演变特征进一步证明,ＥｌＮｉｎｏ事件对应于弱季风,而拉尼娜（ＬａＮｉｎａ）事件对应于强季风。     

中国沿海GPS站用于潮波系数提取分析

首先给出了基于GNSS-MR技术提取潮波系数的原理与方法,然后利用布设在浙江省石浦港验潮室屋顶的GPS站DSPU实测数据对潮波系数进行了提取,并与验潮站实测潮位调和分析结果进行了对比分析。实验结果表明GPS-MR反演潮位与验潮站实测潮位值吻合较好,相关系数优于0.97;GPS-MR反演潮位与验潮站实测潮位获取的潮波系数基本一致,除M2、S2外其它差异较小。两者获取的潮波系数差异主要因为DSPU测站观测环境极大地影响了GPS-MR提取潮位精度。沿海GNSS站用于潮位监测和潮波系数提取,将进一步拓展沿海GNSS监测站的应用领域,在一定程度上可弥补验潮站的不足。     

特种监测网解算中IGS站选取的探讨

利用GAMIT/GLOBK软件处理2011年特种监测网的观测数据,将IGS站的选取分10种方案进行试验,计算结果从基线长度变化、测站坐标、NRMS、基线重复率等方面进行了比较。得出利用GAMIT/GLOBK软件处理该特种监测网时的几点建议:建议选取IGS站为GPS网提供参考框架;IGS站选取的数量不一定最多,但空间上应尽量均匀分布;选取7个IGS站时得到较高精度的结果。在日后处理此类型的特种监测网时对IGS站选取具有一定的参考价值。     

Development of a regional neural network for coastal water level predictions

This paper presents the development of a Regional Neural Network for Water Level (RNN_WL) predictions, with an application to the coastal inlets along the South Shore of Long Island, New York. Long-term water level data at coastal inlets are important for studying coastal hydrodynamics sediment transport. However, it is quite common that long-term water level observations may be not available, due to the high cost of field data monitoring. Fortunately, the US National Oceanographic and Atmospheric Administration (NOAA) has a national network of water level monitoring stations distributed in regional scale that has been operating for several decades. Therefore, it is valuable and cost effective for a coastal engineering study to establish the relationship between water levels at a local station and a NOAA station in the region. Due to the changes of phase and amplitude of water levels over the regional coastal line, it is often difficult to obtain good linear regression relationship between water levels from two different stations. Using neural network offers an effective approach to correlate the non-linear input and output of water levels by recognizing the historic patterns between them. In this study, the RNN_WL model was developed to enable coastal engineers to predict long-term water levels in a coastal inlet, based on the input of data in a remote NOAA station in the region. The RNN_WL model was developed using a feed-forwards, back-propagation neural network structure with an optimized training algorithm. The RNN_WL model can be trained and verified using two independent data sets of hourly water levels.The RNN_WL model was tested in an application to Long Island South Shore. Located about 60–100 km away from the inlets there are two permanent long-term water level stations, which have been operated by NOAA since the1940s. The neural network model was trained using hourly data over a one-month period and validated for another one-month period. The model was then tested over year-long periods. Results indicate that, despite significant changes in the amplitudes and phases of the water levels over the regional study area, the RNN_WL model provides very good long-term predictions of both tidal and non-tidal water levels at the regional coastal inlets. In order to examine the effects of distance on the RNN_WL model performance, the model was also tested using water levels from other remote NOAA stations located at longer distances, which range from 234 km to 591 km away from the local station at the inlets. The satisfactory results indicate that the RNN_WL model is able to supplement long-term historical water level data at the coastal inlets based on the available data at remote NOAA stations in the coastal region.     

埕岛油田及附近地区的水位

在渤海内,M2分潮有两个无潮点,一个在秦皇岛东面,另一个位于渤海湾至莱州湾之间。此外,渤海海峡附近还有一个K1分潮的无潮点。埕岛油田位于M2分潮无潮点和K1分潮波的腹部附近。整个石油开发区半日潮差均较小,其中以东南部最小,西北部有明显增加。全日潮差由东北向西南增加,变化幅度较小。在一般天气条件下,海面高度主要决定于潮汐,潮差不大,但在台风影响下,可能引起异常的增水,寒潮可以引起大的减水或增水。国内不少单位在黄河口做过潮汐观测,但资料甚少。埕岛油田位于黄河口的西北部,海上水位资料十分缺乏。我们利用12井位和20井位进行了4个月的水位观测,使用Aanderaa水位计,取样间隔为15min。岸边站(3站)是在岸边打桩,使用滚筒式水位计,获得了一年的水位观测资料,这些资料十分宝贵。本文详细地分析了该区域的水位特征,并给出施工设计需要的各个参数。     

Biodiversity and seasonal variation of benthic macrofauna in Minicoy Island, Lakshadweep, India

From the Indian coast only limited data are available on the benthic fauna of the seagrass communities. In this study, seasonal variation in the distribution of macrobenthos and influence of environmental parameters was explored at four seagrass beds and two mangrove stations along the Minicoy Island, Lakshadweep, India, from September 1999 to August 2001. A total of 160 macrobenthic species from eight major groups represented the macrofauna of the Minicoy Island. Of the identified taxa, molluscs 70（gastropods 41.46%, bivalves 7.5%）, polychaetes 27（16.88%）, crustaceans 30（18.75%）, echinoderms 11（6.88%） and remaining others. Average seasonal abundance of benthic macrofauna ranged from 219 to 711 ind./m2, species diversity varied from 1.45 to 3.64 bits per individual, species richness index ranged from 4.01 to 26.17, evenness 0.69 to 1.66. In general, the higher abundance and species diversity was noticed in southern seagrass stations and northern seagrass stations, but in the mangrove stations comparatively low species diversity was observed. Three-way analysis of variance indicated that all communities resulted as being significantly different between seagrass and mangrove station, mainly when the seasonal interaction was considered. Multivariate analyses were employed to help define benthic characteristic and the relationship between environmental parameters at the six monitoring stations. Results of cluster analyses and multidimensional scale plot suggest that for mangrove region, different physiographic provinces, lower salinity, dissolved oxygen and sediment biotic structure have a higher influence on the species composition and diversity than other oceanographic conditions.