河流营养物参照状态的建立方法研究进展

建立营养物参照状态是河流水污染控制的关键问题之一。系统论述了参照状态的各种内涵,重点分析了国外建立河流营养物参照状态的各种方法及优缺点,并分析了其在中国的适用性。最后,提出了河流营养物参照状态的进一步研究的重点。参照状态根据允许人类活动影响的程度可有多种含义:最小干扰状态、历史状态、最少干扰状态和最佳可达成状态。其中,最少干扰状态和最佳可达成状态在现实管理中具有一定程度的可操作性。参照河流百分比法是建立营养物参照状态的首选方法,但中国水环境污染形势严峻,参照点变得越来越少。当参照点不存在时,一般河流百分比法是参照河流百分比法的替代方法,然而一般河流百分比法和参照河流百分比法的匹配性关系并不完全保持一致。由于栖息地退化等因素对生物完整性的影响程度可能比营养物浓度更大,生物响应法在实际应用中是非常困难的。流域模型法虽然有众多优点,但是数据要求较高,并且常用流域模型的机理与中国有较大差距,在中国应用的结果具有较大的不确定性。综合考虑中国水环境污染现状和数据要求,以多元线性回归模型为代表的简易模型方法在中国应有最大的适用性。然而,环境因素与营养物质间的关系往往都是非线性的,今后应着重研究建立河流营养物参照状态的多元非线性回归模型方法。此外,还应加强季风河流营养物参照状态的季节性差异与年际差异研究,并在全国层面上根据自然因素划分适当规模的生态区,分区确定各生态区的河流营养物参照状态。     

线性规划方法在区域发展规划环境影响评价中的应用

文章介绍了区域产业规模最优化发展的线性规划模型,解释了模型的具体含义并给出了模型中各参数的确定依据和方法,并在某区域发展规划环境影响评价中进行了应用。通过该模型得出的最优方案与规划方案的对比,可以评价规划方案的环境合理性,同时也可为区域产业发展提出调整建议,实现区域经济效益和环境效益的统一,最终实现区域的可持续发展。     

地震人员伤亡快速评估模型研究

每年地震都会给社会造成巨大人员伤亡,如果震后能迅速预知地震人员伤亡数量,这对挽救生命损失是十分有意义的。统计了近10年来中国大陆数10次强震、中强震灾害损失,根据影响伤亡的主要因素,用线性回归分析法,建立地震人员伤亡快速评估模型,并对模型进行验证,使之可用于地震预警。     

林聪悟-直线法在基于美国混凝土规范设计中的程序实现

首先,阐述了美国ACI318-08规范中钢筋混凝土受弯、受压构件的设计原理和基本假定。与我国混凝土结构设计规范不同,ACI318-08规范是根据构件的受力破坏形态来确定名义抗压和抗弯承载力的折减系数的,相对于延性破坏,脆性破坏构件的承载力折减较多。然后,由平衡条件、平截面假定及Newton迭代格式,推导出林聪悟-直线法的计算公式。编制程序实现该算法时,须根据最大、最小配筋率的规定对迭代区域进行限定;在确定P-M相关曲线上的拐点位置的基础上,实现中性轴高度在曲线上的分段迭代。通过在曲线拐点处设置微小增量及自动调整中性轴高度等方法,解决了程序计算时可能出现的迭代发散问题。算例表明,所编制程序的计算结果具有很高的精度。     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Evaluating the ability of habitat suitability models to predict species presences

Models predicting species spatial distribution are increasingly applied to wildlife management issues, emphasising the need for reliable methods to evaluate the accuracy of their predictions. As many available datasets (e.g. museums, herbariums, atlas) do not provide reliable information about species absences, several presence-only based analyses have been developed. However, methods to evaluate the accuracy of their predictions are few and have never been validated. The aim of this paper is to compare existing and new presence-only evaluators to usual presence/absence measures.     

Fuzzy optimization of multi-period carbon capture and storage systems with parametric uncertainties

Carbon capture and storage (CCS) is an important technology option for reducing industrial greenhouse gas emissions. In practice, CO₂ sources are easy to characterize, while the estimation of relevant properties of storage sites, such as capacity and injection rate limit (i.e., injectivity), is subject to considerable uncertainty. Such uncertainties need to be accounted for in planning CCS deployment on a large scale for effective use of available storage sites. In particular, the uncertainty introduces technical risks that may result from overestimating the limits of given storage sites. In this work, a fuzzy mixed integer linear program (FMILP) is developed for multi-period CCS systems, accounting for the technical risk arising from uncertainties in estimates of sink parameters, while still attaining satisfactory CO₂ emissions reduction. In the model, sources are assumed to have precisely known CO₂ flow rates and operating lives, while geological sinks are characterized with imprecise fuzzy capacity and injectivity data. Three case studies are then presented to illustrate the model. Results of these examples illustrate the tradeoff inherent in planning CCS systems under parametric uncertainty.     

Preliminary shotgun glycoproteome analysis of N-glycosylation sites in serum proteins of ricin-intoxicated rats

In this study, the variations of serum glycoproteins after exposure to ricin toxin (RT) in Wistar albino rats are reported. For glycopeptide enrichment, Microcon YM-3 centrifugal filter device capture and micro-liquid chromatography (LC)–electrospray ionization (ESI)–tandem mass spectrometric (MS/MS) analysis were used; 74 trypsin-digested proteins were identified in the control group, and 58 in the RT-intoxication group. Additionally, 33 N-glycosides and 14 glycoproteins were identified in the control, and 50 and 21 in the RT-intoxication group.     

基于TLSER和QSPR关系预测PAHs和PCBs的LDPE膜-空气分配系数

有机污染物在被动采样材料与环境介质之间的平衡分配系数(K_P),是测定环境中有机污染物浓度的重要参数,但K_P值大部分都需要经过繁琐的实验测定获取,无法逐个测定数量繁多的污染物,因此需开发一种预测K_P值的方法。为此,搜集整理了一些多环芳香烃(PAHs)和多氯联苯(PCBs)的低密度聚乙烯(LDPE)-空气分配系数(K_PA)的实测值,基于理论线性溶解能(TLSER)和定量结构性质关系(QSPR),利用逐步多元线性回归(MLR)分别构建了预测K_PA值的模型。模型的决定系数R2adj分别为0.927和0.956,交叉验证系数Q2LOO分别为0.915和0.946,外部系数Q2ext分别为0.913和0.960。结果表明,2种模型具有良好的拟合优度、稳健性和预测能力,并解释了模型的机理。所构建的2种模型均可用来预测应用域内有机污染物的LDPE膜-空气分配系数。     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.