滦河秋季大型底栖动物群落结构空间格局与关键环境影响因子识别

掌握水生生物的空间分布特征及其关键影响因子是科学制定生物多样性保护策略的基础.于2016年秋季对滦河53个样点开展了大型底栖动物和环境因子的现场调查,分析了大型底栖动物群落结构与空间分布特征,并识别出影响大型底栖动物物种空间分布的关键环境因子.结果表明:①滦河（河北省）共采集并鉴定出大型底栖动物206种,隶属于8纲22目70科144属,以纹石蚕（Hydropsyche sp.）和东方蜉蝣（Ephemera orientalis）为优势种.②根据滦河大型底栖动物群落结构相似性,53个样点在空间上被分为3组.第1组分布在滦河干流的中下游及支流下游,无指示物种;第2组分布在支流上游,指示种为纹石蚕、朝大蚊（Antocha sp.）和中华小长臂虾（Palaemonetes sinensis）;第3组分布在滦河干流中上游,指示种为东方蜉蝣和中华齿米虾（Neocaridina denticulate sinensis）.③第3组的分类单元数显著高于第1组和第2组;第1组的多样性指数显著高于第2组;第1组的均匀度指数显著高于其他2组.单因素方差分析（one-way ANOVA）发现,pH、TN、底质、河道变化、水质状况和河岸土地利用类型等环境因子在第2组与第3组之间差异显著,第2组明显高于第3组.典范对应分析（CCA）表明,栖境复杂性、河水水量状况和河岸土地利用类型是影响滦河大型底栖动物空间分布的关键环境因子.研究显示,降低河岸带干扰强度、恢复河道生境多样性是未来滦河大型底栖动物多样性保护恢复的重要管理措施.      

辽东湾潜在褐潮生物时空分布及环境关联

以18S rDNA V4区作为目标基因,利用自行设计的真核浮游植物鉴定引物V4（F/R）,结合高通量测序技术,对辽东湾2014年四季海水中真核浮游植物多样性进行了检测.结果发现了8种潜在褐潮生物：抑食金球藻、金牛微球藻、微拟球藻、颗粒微拟球藻、细小微胞藻、密球藻、普通小球藻、钙质角毛藻,其中前2种生态风险较高,主要分布在辽东湾西南、东南两侧.除抑食金球藻和细小微胞藻为混合营养型外,其他6种均为自养型.细小微胞藻丰度最高（平均161445个/L）,主要分布在秋季;其次是抑食金球藻、金牛微球藻（平均13912,13717个/L）,主要分布在春季;之后为钙质角毛藻、普通小球藻（平均5498,5234个/L）,主要分布在秋季;密球藻（平均1345个/L）,主要分布在夏季;微拟球藻、颗粒微拟球藻最少（平均56,44个/L）,冬季无分布.辽东湾潜在褐潮生物丰度与水温、水深、无机氮、N/P显著相关,其群落结构演替与环境因子关联性有待于进一步研究.     

海洋环境低水位加速腐蚀研究进展

以海洋环境中一个非常重要的局部腐蚀形式——低水位加速腐蚀(ALWC)为对象,自其检测、发生原因、防护三个方面就国内外的文献报告进行综述分析。在检测方面,首先介绍了常用的宏观观察这一被动形式,并突出其特征,然后介绍了利用海水中可溶性无机氮含量作为ALWC发生概率预测这一主动形式,分析其优势与不足。在发生原因方面,在对将ALWC认定为一种典型的微生物腐蚀(MIC)形式的认知过程进行介绍之后,重点分析了微生物对ALWC作用机制不清晰的原因,并建议在后续研究中突出动态演变过程,结合高通量测序等分子生物学技术,确定在不同的阶段影响ALWC的关键微生物,且进一步在大气-海水体系下研究典型菌株及其协同作用的影响,提出微生物对ALWC的作用机制。在防护方面,根据新建和已建钢结构设施分别对传统和针对ALWC所具有的MIC与局部腐蚀特性的新型高效防护方法进行了介绍,并分析了防护方法的优缺点。     

发展我国环境监测技术产业的探讨

本文从发展我国环境监测技术产业的角度论述了环境监测技术产业的主要内容，分析了我国环境监测技术产业的发展和存在的问题，对发展我国环境监测技术产业提出了建议。     

石墨炉原子吸收光谱法测定海河下游水中痕量镉

原子吸收光谱法直接测定高盐水中痕量镉时，有很大背景吸收和误差。本文采用络合—萃取技术使共存元素与待测元素分离，既消除了基体干扰，又达到了富集作用，使测定结果准确可靠。     

济南市创建国家环保模范城市的调查与思考

从济南市环境保护现状调查入手,对照国家环境保护模范城市考核指标,分析济南市创建国家环境保护模范城市存在的问题,借鉴其他城市创建国家环境保护模范城市的成功经验,结合济南市实际,提出了有针对性的对策与建议。     

锅炉环境影响评价系统软件研究

用Ｃ语言作为开发语言，研制开发了锅炉环境影响评价系统软件，软件充分利用Ｃ语言模块化，过程化的优点，源程序由１２个模块组成。经编译连接生成一个可执行程序，用户只需输入１５个必需的原始参数，软件即可自动查询，计算大量的中间参数，并立即给出污染源强及污染物在周围环境中分布的预测和评价结果。     

经济手段防治白色污染的博弈分析

经济手段在环境管理中的作用及涉及到政府与各利益关系人之间的博弈过程。本文就政府与一次性餐具生产者，销售者之间的博弈关系，分析如何应用经济手段使其在防治“白色污染”中发挥更好的作用。     

A pilot project to detect and forecast harmful algal blooms in the northern Gulf of Mexico

More timely access to data and information on the initiation, evolution and effects of harmful algal blooms can reduce adverse impacts on valued natural resources and human health. To achieve this in the northern Gulf of Mexico, a pilot project was initiated to develop a user-driven, end-to-end (measurements to applications) observing system. A key strategy of the project is to coordinate existing state, federal and academic programs at an unprecedented level of collaboration and partnership. Resource managers charged with protection of public health and aquatic resources require immediate notice of algal events and a forecast of when, where and what adverse effects will likely occur. Further, managers require integrated analyses and interpretations, rather than raw data, to make effective decisions. Consequently, a functional observing system must collect and transform diverse measurements into usable forecasts. Data needed to support development of forecasts will include such properties as sea surface temperature, winds, currents and waves; precipitation and freshwater flows with related discharges of sediment and nutrients; salinity, dissolved oxygen, and chlorophyll concentrations (in vivo fluorescence); and remotely-sensed spatial images of sea surface chlorophyll concentrations. These data will be provided via a mixture of discrete and autonomous in situ sensing with near real-time data telemetry, and remote sensing from space (SeaWiFS), aircraft (hyperspectral imagery) or land (high-frequency radar). With calibration across these platforms, the project will ultimately provide a 4-dimensional visualization of harmful algae events in a time frame suitable to resource managers.     

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.