环保电子政务建设中网络安全管理

结合沈阳市环保局电子政务网络,介绍在网络信息系统安全性和保密性方面采取的一些方法和策略,主要包括网络安全的物理隔离和逻辑隔离,网络边界安全控制,集中网络安全管理,数据安全,网络病毒防治,环境安全,安全管理措施等。     

人类活动对河流沉积物中反硝化厌氧甲烷氧化菌群落特征的影响

河流生态系统是陆地生态系统输出营养盐和有机质的主要接收器,是水-气界面CO2和CH4全球碳循环的重要环节.人类活动导致大量未经处理的硝酸盐和有机物质汇入河流,影响了N-DAMO(N-DAMO,Nitrate/nitrite-dependent anaerobic methane oxidation,反硝化厌氧甲烷氧化菌)细菌的群落特征.本文选取北运河作为研究区域,通过对比分析北运河中游和下游沉积物理化参数和N-DAMO细菌群落特征的差异性,探究由于人类活动的影响,河流沉积物中N-DAMO细菌的群落组成结构特征,及其与沉积物中NH+4-N、NO-3-N的响应关系.结果表明,北运河沉积物中NH+4-N为中游和下游氮素的主要形态,且下游NH+4-N浓度显著高于中游;人类活动对N-DAMO细菌16S rRNA和pmo A功能基因群落分布有影响,16S rRNA和pmo A功能基因均分别聚为中游和下游两类;系统发育树分析显示,人类活动影响北运河N-DAMO细菌高同源性菌群的来源,其高同源性菌群来源与北运河主要污染物氨氮的来源一致;RDA分析显示,人类活动影响N-DAMO细菌相关环境因子,沉积物中高浓度的NH+4-N、NO-3-N与16S rRNA和pmo A功能基因有显著的响应关系.沉积物N-DAMO细菌16S rRNA和pmo A功能基因的共生关系分析显示,北运河下游沉积物中N-DAMO细菌彼此之间的共存关系更强,细菌群落形成的模块化程度较高,其对环境变化的敏感程度更高,受人类活动的影响更大.     

Characteristics of microbial community involved in early biofilms formation under the influence of wastewater treatment plant effluent

Effluents from wastewater treatment plants (WWTPs) containing microorganisms and residual nutrients can influence the biofilm formation. Although the process and mechanism of bacterial biofilm formation have been well characterized, little is known about the characteristics and interaction of bacteria, archaea and eukaryotes in the early colonization, especially under the influence of WWTP effluent. The aim of this study was to characterize the important bacterial, archaeal and eukaryotic species in the early stage of biofilm formation downstream of the WWTP outlet. Water and biofilm samples were collected 24 and 48 hr after the deposition of bio-cords in the stream. Illumina Miseq sequencing of the 16S and 18S rDNA showed that, among the three domains, the bacterial biofilm community had the largest alpha and beta diversity. The early bacterial colonizers appeared to be “biofilm-specific”, with only a few dominant operational taxonomic units (OTUs) shared between the biofilm and the ambient water environment. Alpha-proteobacteria and Ciliophora tended to dominate the bacterial and eukaryotic communities, respectively, of the early biofilm already at 24 hr, whereas archaea played only a minor role during the early stage of colonization. The network analysis showed that the three domains of microbial community connected highly during the early colonization and it might be a characteristic of the microbial communities in the biofilm formation process where co-occurrence relationships could drive coexistence and diversity maintenance within the microbial communities.     

长期秸秆还田对潮土真菌群落、酶活性和小麦产量的影响

为了研究秸秆还田对黄淮海平原潮土真菌群落的影响,开展连续10 a田间定位试验,设置不施肥（NF）、单施化肥（NPK）和秸秆还田配施化肥（NPKS）这3个处理,通过网络分析和结构方程模型,揭示真菌群落对土壤肥力、酶活性和小麦产量的影响机制.结果发现,NPKS处理土壤有机质（SOM）较NPK处理和NF处理分别升高9.20%和34.75%,碱解氮（AN）升高12.03%和39.17%,脱氢酶（DHA）升高37.21%和50.91%,β-葡糖苷酶（β-GC）升高17.29%和73.48%,小麦增产16.22%和125.53%.不同施肥处理真菌α-多样性并未发生显著变化,却导致β-多样性出现明显分异.冗余分析表明,速效磷（AP）、SOM和AN是潮土真菌群落组成变化的主要调控因素.差异物种分析表明,NF处理富集了Mortierella、Aspergillus、Ceriporia和Acremonium等具有溶磷/解钾功能的物种,以及Leohumicola和Hyalodendriella等植物共生菌;NPK处理中Sarocladium、Fusarium和Fusicolla等植物病原菌丰度显著升高;NPKS处理则激发了Pseudogymnoascus和Schizothecium等抑病菌生长,并提高Trichocladium和Lobulomyces等秸秆降解物种丰度.网络分析发现整个网络由4个主要模块组成,其中模块2物种累积丰度在NPKS处理显著升高,且与DHA和β-GC呈线性正相关关系.结构方程模型结果进一步表明小麦产量主要受SOM直接正向调控,而模块2物种可通过正向调节DHA和β-GC,间接影响SOM和小麦产量.综上所述,黄淮海平原潮土区秸秆还田可通过调节真菌种间互作关系,刺激特定物种集群生长,抑制病原菌活性,提高土壤酶活性,促进SOM累积,最终获得作物高产.     

Modeling changes in the coastal ecosystem of the Pearl River Estuary from 1981 to 1998

The coastal ecosystem of the Pearl River Estuary (PRE) has been overfished and received a high level of combined pollution since the 1980s. Ecopath with Ecosim was used to construct two ecosystem models (for 1981 and 1998) to characterize the food web structure and functioning of the ecosystem. Pedigree work and simple sensitivity analysis were carried out to evaluate the quality of data and the uncertainty of the models. The two models seem reliable with regards to input data of good quality. Comparing the variations of outputs of these two models aimed to facilitate assessment of changes of the ecosystem during the past two decades.The trophic structure of the ecosystem has changed with an increase in the biomass proportion of lower trophic level (TL) organisms and a decrease in top predator biomass proportion. All the indices of ecosystem maturity examined show that the system was in a more mature condition in 1981 than in 1998, although the system has been in a condition of stress due to anthropogenic disturbances, such as environmental pollution and habitat destruction since 1981. The ecosystem was aggregated into six and seven integral TLs in 1981 and 1998, respectively, using the trophic aggregation routine of Ecopath. Most of the total system biomass and catch took place at TL II and III in both years. But the distribution of the total system biomass and catch at different TLs changed with decreasing proportions in higher TLs in 1998. The mean transfer efficiency was 9.1% and 10.2% in 1981 and 1998, respectively.Comparative network analysis allowed quantification of the importance of direct and indirect trophic interactions among functional groups. Moreover, a method derived from the mixed trophic impact (MTI) analysis allowed estimating importance of groups in terms of “keystoneness” and identifying the keystone species in the two models over the past two decades. The results indicate that there were no clear keystone species in 1998 but two keystone species at medium trophic levels were identified in 1981. Moreover, organisms located at low trophic levels such as phytoplankton, zooplankton and benthic invertebrates were identified to have relatively high keystoneness in the ecosystem.     

Cycling in ecosystems: An individual based approach

Cycling index is an important ecological indicator used in ecosystem analysis. The higher the cycling in an ecosystem, the higher the utilization of mass and energy within the system before it is lost due to respiration and other factors. For a stock-flow type ecosystem model at steady state, Finn’s cycling index (FCI) can be computed using simple matrix algebra. However, it is difficult to measure how well this index represents the actual cycling occurring in the system. In this paper, we study cycling in ecological networks using an individual based approach (particle tracking algorithm). This new simulation method provides access to the pathway data of individual particles that flow in the system, therefore one can quantify cycling using this pathway data quite literally. We used particle tracking simulations (PTS) to compute a cycling index using Finn’s idea of flux based cycling. Our simulation based results (using no matrix algebra) agree with Finn’s cycling index, verifying the accuracy of both the PTS, and the original linear algebraic formulation of FCI.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

Mitigating microbiological risks of potential pathogens carrying antibiotic resistance genes and virulence factors in receiving rivers: Benefits of wastewater treatment plant upgrade

● Abundance of MAGs carrying ARG-VF pairs unchanged in rivers after WWTP upgrade. ● Upgrade of WWTPs significantly reduced diversity of pathogenic genera in rivers. ● Upgrade of WWTPs reduced most VF (ARG) types carried by potential pathogens in rivers. ● Upgrade of WWTPs narrowed the pathogenic host ranges of ARGs and VFs in rivers. Wastewater treatment plants (WWTPs) with additional tertiary ultrafiltration membranes and ozonation treatment can improve water quality in receiving rivers. However, the impacts of WWTP upgrade (WWTP-UP) on pathogens carrying antibiotic resistance genes (ARGs) and virulence factors (VFs) in rivers remain poorly understood. In this study, ARGs, VFs, and their pathogenic hosts were investigated in three rivers impacted by large-scale WWTP-UP. A five-year sampling campaign covered the periods before and after WWTP-UP. Results showed that the abundance of total metagenome-assembled genomes (MAGs) containing both ARGs and VFs in receiving rivers did not decrease substantially after WWTP-UP, but the abundance of MAGs belonging to pathogenic genera that contain both ARGs and VFs (abbreviated as PAVs) declined markedly. Genome-resolved metagenomics further revealed that WWTP-UP not only reduced most types of VFs and ARGs in PAVs, but also effectively eliminated efflux pump and nutritional VFs carried by PAVs in receiving rivers. WWTP-UP narrowed the pathogenic host ranges of ARGs and VFs and mitigated the co-occurrence of ARGs and VFs in receiving rivers. These findings underline the importance of WWTP-UP for the alleviation of pathogens containing both ARGs and VFs in receiving rivers.     

基于RPN-BN的平台-隔水管系统遇内波风险分析

为保障内孤立波作用下的深水半潜式钻井平台-隔水管系统的安全，同时解决海洋平台系统设备失效数据的缺失问题，提出1种风险优先系数（RPN）与贝叶斯（BN）结合的定量风险分析方法。首先，基于故障树和安全屏障方法，建立平台-隔水管系统Bow tie模型和贝叶斯风险演化模型；其次，根据贝叶斯推断和风险优先系数中的事故发生频度估计，得到平台-隔水管系统失效事故的发生概率；最后，通过贝叶斯网络的逆向推理能力辨识内孤立波作用下引起平台-隔水管系统失效的主要风险节点，实现对平台-隔水管系统失效事故的定量风险分析。结果表明:RPN-BN法可应用于平台-隔水管系统遇内波的定量风险分析；加强对平台漂移量的控制，提高动力定位系统控制设备的可靠性可有效抵御内波对系统造成的影响。     

基于贝叶斯网络的高速动车组运营故障分析

为研究影响高速动车组正常运行的各故障因素间的因果关系，分析其耦合强度，将故障因素分为人、机、环3类，从系统角度又将机器因素分为5个子系统。人、机、环3类共识别27个故障因素；使用K2算法生成贝叶斯网络结构，引入扩展因果效应算法确定节点优先次序作为K2算法的先验知识，采用EM算法学习贝叶斯网络参数，构建基于贝叶斯网络的高速动车组运营故障分析模型；以209个CRH详细故障报告为例，对故障因素的故障发生概率进行排序并分析因素间的影响强度和灵敏度。结果表明:牵引供电系统故障发生概率较高；车门系统故障、牵引变流器故障易由内部零件故障引起，外界异物击打对受电弓影响较大；人、环因素更易引起多故障耦合；环境因素对牵引供电系统表现出较高的灵敏度。贝叶斯网络在分析高速动车组运营系统故障问题上具有可行性，分析结果有助于提升运营单位的管控能力。