洪泽湖水体富营养化时空分布特征与影响因素分析

通过2014年—2017年对洪泽湖12个水质断面定期调查,采用营养状态指数(TLI)综合评价其水体富营养状态,同时应用主成分分析方法(PCA)分析其富营养化状态的时空变化特征。结果表明,洪泽湖70%以上的调查断面水质全年处于轻度富营养化状态,夏季是其富营养化最严重的季节;洪泽湖年内水体水质差异较大,而其水华特征并未呈现明显差异;洪泽湖富营养化很大程度上受制于营养盐的积累程度,并与湖泊透明度呈现极显著的负相关关系(p0.001),与湖水pH值呈现极显著的正相关关系。     

基于支持向量回归机的南黄海浒苔分布面积预测模型

绿潮作为一种新型的海洋灾害,已经引起了各个国家的重视.依据2012年南黄海海域浒苔遥感监测分布面积数据,选取了温度、天气状况、风向、风力、浪高5种影响浒苔扩散的气候因子,建立了基于SVR的浒苔分布面积预测模型,并与经典的最近邻点插值模型、线性插值模型、3次样条函数插值模型和分段3次Hermite插值模型进行了回归效果的对比.分析结果表明,基于SVR的浒苔分布面积预测模型能够为浒苔遥感数据的插补提供一种方法,且回归效果优于传统的回归方法,为浒苔的防治提供辅助决策信息.     

SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

关于长江产业带建设总体布局的初步思考

重点开发长江是90年代中国经济重大战略布署。将长江产业带建设成具有强大经济实力的国家一级经济轴线,宜采用分层次推进与中心辐射相结合的发展战略,以浦东开发和三峡建设为契机,加强基础产业,发展新兴产业和第三产业,优化产业结构,以能源和交通建设先行,改善基础设施。同时,要协调产业带建设与浦东开发、三峡工程建设的关系。     

长江三峡柑桔的冻害和热害（二）

本文从柑桔冻害和热害的危害因子和指标等级划分的研究和选取入手,着重探讨了长江三峡地区(湖北境内)两害显著的时空变化特征、差异与关联性及对柑桔生产的影响;揭示了80年代以来冬暖春热的重大气候变化是使两害向“两极分化”的根本原因;讨论了三峡水利工程对两害时空格局的可能调整及减灾原理;最后提出了可能的对策。     

湖北省湿地的保护与利用

湿地是地球上具有多功能的、独特的生态系统，是人类最重要的生存环境之一。湖北省地处长江中游，湿地类型多，面积较大，河、湖湿地系统是主要的湿地类型。湖北湿地具有较大的资源潜力和环境调节功能，是自然界生物多样性丰富的生态景观。近年来，湖北省湿地保护面临人口增长的压力，围垦、污染和过度开发等对湿地保护构成了严重威胁，湿地环境遭到干扰和破坏。加强湿地科学的研究，保护管理好湖北湿地及其生态环境，实现湿地资源的持续利用，是湖北省2l世纪环保工作所面临的一项重要而紧迫的任务。     

神农架主峰南坡猕猴桃种质资源调查及保护策略

巴东县北部神农架主峰南坡的送子园原始森林保护区内具有种类繁多的猕猴桃种质资源，经调查发现共分布有中华猕猴桃（A.chiensis)、美味猕猴桃（A.deliciosa)、革叶猕猴桃(A．rubricalis var.coriacea)、葛枣猴桃(A．ploygama)、京梨猕猴桃(A．callosa var．henryi)、软枣猕猴桃(A．arguta)、巴东猕猴桃(A．tetramera var.badongenisis)、紫果猕猴桃(A．arguta var.purpurea)及紫果新变型、绵毛藤山柳(Clematoclthra lanosa)10种(包括变种、变型)。简述了各种类的植物学性状和果实经济性状，种质资源分布的主要特点以及生态环境，指出由于砍伐森林和环境条件的恶化使猕猴桃资源已受到威胁和破坏，提出保护策略的重点是提高全民的生态保护意识；可持续开发利用资源；加大科技对猕猴桃资源保护支撑的力度。其主要保育措施：一是在保护区内就地进一步加强物种全面保护；二是省级以上科研单位分区建立猕猴桃后种质资源圃，对濒危物种或持有物种进行迁地保护；三是开展超低温保存技术的研究，建立种子和花粉基因库。并建立完善与国际接轨的信息数据库平台，便于国际合作与交流。     

Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003

Data collected from the five air-quality monitoring stations established by the Taiwan Environmental Protection Administration in Taipei City from 1994 to 2003 are analyzed to assess the temporal variations of air quality. Principal component analysis (PCA) is adopted to convert the original measuring pollutants into fewer independent components through linear combinations while still retaining the majority of the variance of the original data set. Two principal components (PCs) are retained together explaining 82.73% of the total variance. PC1, which represents primary pollutants such as CO, NO(x), and SO(2), shows an obvious decrease over the last 10 years. PC2, which represents secondary pollutants such as ozone, displays a yearly increase over the time period when a reduction of primary pollutants is obvious. In order to track down the control measures put forth by the authorities, 47 days of high PM(10) concentrations caused by transboundary transport have been eliminated in analyzing the long-term trend of PM(10) in Taipei City. The temporal variations over the past 10 years show that the moderate peak in O(3) demonstrates a significant upward trend even when the local primary pollutants have been well under control. Monthly variations of PC scores demonstrate that primary pollution is significant from January to April, while ozone increases from April to August. The results of the yearly variations of PC scores show that PM(10) has gradually shifted from a strong correlation with PC1 during the early years to become more related to PC2 in recent years. This implies that after a reduction of primary pollutants, the proportion of secondary aerosols in PM(10) may increase. Thus, reducing the precursor concentrations of secondary aerosols will be an effective way to lower PM(10) concentrations.     

九龙江河口春季磷的形态分配及浮游植物对磷的响应

以石码河口及上游水域为研究对象，2007年5月对表层水中不同形态的磷及浮游植物生物量叶绿素a（Chla）进行了调查，分析河口磷形态分配的影响因素及与浮游植物生物量的关系。以颗粒态有机磷（POP）为主的颗粒态磷（PP）占总磷的61．2％；溶解态无机磷（DIP）占溶解态磷（TDP）的72．8％。河口区上游磷与河口磷的组成相似，雨水的淋洗作用对水体中磷的组成有较大影响，以颗粒态无机磷（PIP）的显著增高为特征。水中Chla与TP和DOP存在极显著的正相关，表明生物活动在一定程度上控制着该水域表层水中磷形态的分布。