Adding complex trophic interactions to a size-spectral plankton model: Emergent diversity patterns and limits on predictability

A new model in the NPZ (nutrient-phytoplankton-zooplankton) style is presented, mechanistically simple but with 40 size classes each of phytoplankton (1-20 μm) and small zooplankton (2.1-460 μm), in order to resolve one level of trophic interactions in detail. General, empirical allometric relationships are used to parameterize both the optimal prey size and size selectivity for each grazer class, as is rarely done. This inclusion of complex predator-prey linkages and realistic prey preferences yields a system with an emergent pattern of phytoplankton diversity consistent with global ocean observations, i.e., a parabolic relationship between diversity (as measured by the Shannon evenness) and biomass. It also yields significant long-term time evolution, which places limits on the extent to which the community response to nutrient forcing can be predicted from forcing in a pragmatic sense. When a simple annual cycle in nutrient supply is repeated exactly for many years, transient fluctuations up to a factor of two in spring bloom magnitude persist for 10-20 years before a stable seasonal biomass cycle is achieved. When the amplitude of the nutrient-supply annual cycle is given a random interannual modulation, these long-lived transients add significant noise to a 100-year correlation between annual-mean nutrient supply and annual-mean biomass. This noise is 20% of total interannual variance in the model base case, and ranges from 0% to 40% depending on the grazer size selectivity. In general, unpredictability on the bloom timescale is damped when food-web complexity is increased by making grazers less selective, while unpredictability on the interannual scale shows the opposite pattern, increasing with increasing food-web complexity up to a high threshhold, past which community structure and biomass time evolution both suddenly simplify. These results suggests a new strategy for ensemble ecosystem forecasting and uncertainty estimation, analogous to methods common in circulation and climate modeling, in which internal variability (predator-prey interactions in the biological case; eddies and climate-system oscillations in the physical case) are resolved and quantified, rather than suppressed.     

分层型水源水库溶解性有机物性质及其膜污染特性

利用激发-发射矩阵(EEM)荧光光谱和紫外吸收光谱研究了深水型水源水库热分层期溶解性有机物(DOM)性质及其膜污染特性随水深的变化.结果表明,水体热分层导致DOM质量浓度和性质也表现出分层特征.变温层受光化学降解影响较大,DOM质量浓度较低,同时受藻类等浮游植物分泌的内源有机物影响,DOM芳香度较低,类富里酸有机物(C1组分)和类腐殖酸有机物(C2组分)荧光强度较低,但类色氨酸有机物(C3组分)荧光强度较高;斜温层DOM受径流输入影响较大,DOM质量浓度和芳香度较高,C1和C2组分荧光强度较高.膜污染方面,变温层DOM造成的总污染最大,但可逆性较好,斜温层和等温层DOM造成的总污染较低,但可逆性较差;对超滤过程中不同荧光组分迁移的分析表明,超滤膜对C3组分截留率较高,但反冲洗对被截留的C3组分去除效果较好,而被膜截留的C1和C2组分较难被反冲洗去除.     

轮叶黑藻对铅的吸附特征及生物吸附机理研究

研究了轮叶黑藻对重金属铅的吸附特征,同时对吸附机理进行探讨.动力学研究结果表明,轮叶黑藻对铅有较快的吸附能力,10min后铅的去除率达到74.54%;20min后,吸附达到平衡.整个吸附过程符合伪二级动力学方程(R2=0.9910).Sips和Langmuir模型相比Freundlich而言,有着较好的拟合效果,表明轮叶黑藻对重金属铅的吸附属于单层吸附,相邻铅离子间的相互干扰可以忽略不计.红外光谱分析表明:轮叶黑藻叶片富含多种活性基团,羟基、羰基和羧基、C–O及C–N为主要作用基团.吸附铅后,轮叶黑藻叶片内K、Na、Ca、Mg含量明显下降,表明铅离子因产生离子交换而被吸附,且铅离子更易与二价的Ca和Mg产生离子交换.     

DC反应器处理中药废水过程中出水的光谱分析

采用三维荧光(EEM)与傅立叶红外(FTIR)光谱技术,对不同运行条件下,新型双循环(DC)厌氧反应器处理中药废水的出水情况进行了分析,以期为厌氧反应器的有效监控提供新思路.结果表明,在HRT为24h时,COD去除率为94%左右,平均产气率达到0.37m3CH4/kgCOD,出水的EEM光谱中,Ex/Em=420/470nm处出现了明显的辅酶F420吸收峰;而HRT减少为12h后,虽然COD去除率仍达到90%左右,但出水EEM光谱中,辅酶F420的吸收峰有所降低,并出现了类富里酸的吸收峰,表明不宜再继续减少HRT.当温度由30oC降低为20oC时,温度的变化影响了厌氧污泥的性状,使得出水的FTIR光谱中3400cm-1处的吸收峰由原来的钝峰变为尖峰.而在进水中添加大黄酸后,造成了反应器内有机酸的累积,产甲烷菌活性受到严重影响,出水的EEM光谱中,腐殖酸与类富里酸的吸收峰显著增强.     

藻源型湖泛发生过程水色变化规律

利用Y-型沉积物再悬浮发生模拟装置,模拟湖泛发生过程,分析水体吸收特性变化特征;同时,利用Hydrolight和CIE颜色匹配函数模拟水体颜色,分析湖泛水色变化规律.结果表明:在湖泛发生过程中,可溶性有色物质(CDOM)浓度(ag)不断增大,无机颗粒物浓度及吸收(ad)总体呈减小的趋势,而浮游植物色素浓度及吸收(aph)随时间变化不规律;基于Hydrolight模拟湖泛水体,离水辐亮度(Lw)和遥感反射比(Rrs)均随时间不断变小;3)另外,随SPIM或ag(443)的变大,水体颜色逐渐由绿色变为棕色.当SPIM增至40mg/L时,水体呈现棕色;当ag(443)达到10m-1时,水体呈现红棕色.通过研究湖泛发生过程水体光学特性和水色变化规律,有助于构建高精度的湖泛遥感监测模型.     

各向异性反射对基于光谱混合分析的植被盖度估算的影响

光谱混合分析法是解决亚像元植被盖度反演估算问题最行之有效的方法,线性光谱混合分析又是其中最常用的研究手段。传统观点认为基于线性光谱混合分析的植被盖度估算精度主要受多重散射和端元光谱可变性的影响,而且各向异性反射这个地物表面所固有的特性也常常被忽略。研究设计了由寸苔草、沙和水3种地物类别组成的棋盘式混合地物反射率光谱测试方案,在探讨分析传统的多重散射与端元光谱可变性的基础上,进一步分析评价了各向异性反射对基于光谱混合分析的植被盖度估算的影响。结果表明,各向异性反射是不可忽视的,在最小化多重散射及端元光谱可变性的影响后,考虑各向异性反射后寸苔草盖度估算精度显著提升,均方根误差RMSE下降了52%。研究不仅证实了各向异性反射在植被盖度估算问题中的重要性,还印证了其是同种端元类型光谱差异性的另一重要来源,拓宽了端元光谱可变性的范畴,为利用光谱混合分析法更准确地进行植被盖度估算提供了新的思路和方法。     

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

• UV-vis absorption analyzer was applied in drainage type online recognition. • The UV-vis spectrum of four drainage types were collected and evaluated. • A convolutional neural network with multiple derivative inputs was established. • Effects of different network structures and input contents were compared. Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%.     

基于抛物线内插的反应谱计算公式及其精度分析

基于反应谱的基本概念,对基于抛物线内插法的反应谱计算公式进行了严格的推导,并给出了详细的显式表达式,建立了当计算时间等步长离散时的基于抛物线内插法的反应谱计算连锁公式。通过对比简谐波输入时的反应谱计算结果,证明基于抛物线内插法的连锁公式的计算结果比广泛应用的反应谱计算方法"精确法"计算的结果更接近于解析解,有更高的计算精度。同时对多条实际记录的地震加速度时程进行了反应谱对比计算,结果表明该公式在计算反应谱时也是稳定的。作者同时发现,"精确法"计算的反应谱值与实际值相比一般是偏小的。     

ESTIMATION OF OPTICAL PROPERTIES OF WATER FROM SECCHI DISK DEPTHS1

ABSTRACT: Optical information on a water body is often required when only Secchi disk depths are available. Many limnologists and water managers have attempted to estimate diffuse light attenuation in water from Secchi depth data assuming a simple inverse relationship. However, we show theoretically that the product of Secchi depth and the diffuse attenuation coefficient is markedly dependent on the reflectance coefficient (“brightness”) of water. Data from 28 New Zealand lakes of diverse optical character demonstrated this dependence over a wide range of reflectance (1.1 to 35.9 percent). Uncritical estimation of attenuation coefficients for diffuse light from Secchi depths, using the currently available simple inverse expressions, is discouraged because of the possibility of bias.     

浒苔反射光谱库系统设计与实现

浒苔光谱特征是遥感之本,可为浒苔灾害的遥感实时监测与预警研究提供重要基础数据。本文基于Delphi开发工具和Interbase数据库平台,采用"面向应用"的系统开发方法,运用dbExpress、DataSnap技术和TeeChart组件,设计并实现了浒苔反射光谱数据库系统,利用本系统可以实现对海量浒苔光谱数据及其属性信息的共享、信息查询、可视化分析和管理等功能。