Climate readiness of recovery plans for threatened Australian species

The rapidly changing climate is posing growing threats for all species, but particularly for those already considered threatened. We reviewed 100 recovery plans for Australian terrestrial threatened species (50 fauna and 50 flora plans) written from 1997 to 2017. We recorded the number of plans that acknowledged climate change as a threat and of these how many proposed specific actions to ameliorate the threat. We classified these actions along a continuum from passive or incremental to active or interventionist. Overall, just under 60% of the sampled recovery plans listed climate change as a current or potential threat to the threatened taxa, and the likelihood of this acknowledgment increased over time. A far smaller proportion of the plans, however, identified specific actions associated with ameliorating climate risk (22%) and even fewer (9%) recommended any interventionist action in response to a climate-change-associated threat. Our results point to a disconnect between the knowledge generated on climate-change-related risk and potential adaptation strategies and the extent to which this knowledge has been incorporated into an important instrument of conservation action.     

Changes in fetal heart rate pattern after intrauterine paracentesis in one twin with meconium peritonitis

We report a case of in utero paracentesis of ascites in a fetus with meconium peritonitis due to volvulus at 34 weeks which resulted in the correction of an abnormal fetal heart rate pattern and enabled vaginal delivery by preventing abdominal dystocia. The intrauterine intervention also helped to establish the diagnosis and potentially reduced the respiratory compromise after birth. Copyright © 2001 John Wiley & Sons, Ltd.     

碘化阶层孔氧化硅纳米球对水中典型有机氯污染物的吸附性能研究

采用共缩聚法,成功地原位合成了碘改性的阶层多孔氧化硅纳米球（SiO₂-I）,研究了其基于卤键作用对典型有机氯污染物六六六的吸附性能,并考察了改性剂I-硅烷含量和pH值对吸附效果的影响.实验结果表明,该纳米材料对六六六表现出优异的吸附富集性能,吸附速率快,60 min内对六六六的去除率为71.6%,240 min内达到吸附平衡,去除率可达98.3%,最大吸附量为178.6 mg·g^-1;吸附动力学符合拟二级动力学模型;碘物种的加入提高了阶层多孔氧化硅的吸附速率和吸附效率.另外,为了便于纳米吸附材料的分离,本研究对SiO₂-I纳米球进行了磁性化.研究发现,磁性化修饰后,SiO₂-I纳米材料仍然保留对六六六优异的吸附富集性能,240 min时的吸附去除率达90.3%.     

保定市大气污染特征和潜在输送源分析

保定市是京津冀地区重要城市之一.为了解保定市大气污染物质量浓度特征和潜在输送源,对保定市国控点2017年1月1日-12月31日PM₁₀、PM_2.5、SO₂、NO₂、O₃、CO等常规大气污染物数据进行分析,并利用TrajStat后向轨迹模型进行区域传输研究.结果表明:①ρ（PM₁₀）、ρ（PM_2.5）、ρ（SO₂）、ρ（NO₂）分别为（138±96）（84±66）（29±23）和（50±24）μg/m3,与2016年相比分别下降5.9%、9.1%、25.5%和13.1%;ρ（CO）较2016年下降了14.0%;ρ（O₃）较2016年增长了25.2%.ρ（PM₁₀）、ρ（PM_2.5）、ρ（NO₂）和ρ（O₃）分别超过GB 3095-2012《环境空气质量标准》二级标准限值的0.97、1.40、0.25和0.34倍,ρ（SO₂）和ρ（CO）未超标.②除ρ（O₃）外,其他污染物质量浓度均呈冬季最高、夏季最低的季节性特征,其中,冬季PM_2.5污染最为严重,春季PM_2.5~10（粗颗粒物）污染严重.③空气质量模型源解析结果显示,保定市ρ（PM_2.5）约60.0%~70.0%来自本地污染源排放.后向轨迹结果表明,在外来区域传输影响中,保定市主要受到西北方向气团（占比为21.7%~60.0%）远距离传输和正南方向气团（占比为34.8%~50.5%）近距离传输的影响.④PSCF（潜在源贡献因子分析法）和CWT（浓度权重轨迹分析法）分析表明,除保定市及周边区县本地污染贡献外,位于太行山东麓沿线西南传输通道的邯郸市、邢台市、石家庄市是影响保定市PM_2.5的主要潜在源区.研究显示,PM_2.5为保定市大气中的主要污染物,并呈冬季高、夏季低的变化特征,其主要来自西北远距离输送和南部近距离传输.      

人工智能技术对长江流域水污染治理的思考

随着经济的快速发展和城市化进程的不断加速,促使水污染严重的长江流域需从污染物去除过程的建模与优化、污水处理过程的优化控制、水污染监测系统的构建开展水污染治理研究.传统的水污染处理技术存在污染物去除效率预测精度较低、污水优化控制成本较高、水污染监测滞后效应严重的问题.人工智能技术能够有效克服上述问题,因此通过梳理国内外学者利用人工智能技术在污水污染物去除过程的建模与优化、污水处理过程的优化控制及水污染监测系统的构建等方面的研究成果,为全面加强长江流域水污染治理能力提供科学可靠的技术指导.结果表明:①利用人工神经网络技术（径向基神经网络、多层前馈网络-人工神经网络、多层感知器神经网络）对污水污染物去除过程进行建模与优化,为精确预测长江流域重金属（Cr、Cu）、营养盐（TN、TP）、持久性有机污染物〔PBDEs（多溴二苯醚）、HCH（六氯环己烷）〕的去除率提供重要参考价值.②采用污水处理的自动控制技术与人工智能技术（递归神经网络、支持向量机、模糊神经网络等）构建污水智能控制系统,为长江流域实现高效节能的污水优化控制提供重要的技术指导.③利用在线监测仪器和人工智能技术（小波神经网络、多元线性回归-人工神经网络、叠层去噪自动编码器等）建立水污染智能监测系统,为解决长江流域水污染监测响应滞后问题提供有力的技术支持.因此,人工智能技术对长江流域提高污水污染物去除率,降低污水优化控制成本,提升水污染监测时效性具有重要的推广价值.      

Pt负载暴露(001)晶面Bi2WO6催化剂合成及光催化性能

通过水热法制备了暴露（001）晶面的Bi₂WO₆纳米片,利用光还原法将Pt纳米颗粒负载于其表面.选择苯甲醇氧化和罗丹明B（RhB）降解为探针反应,评价了催化剂的光催化性能.在苯甲醇氧化实验中,Pt负载暴露001晶面的Bi₂WO₆样品的苯甲醇转化率为20.7%,约为未负载样品的2倍.在RhB降解实验中,Pt负载样品在光照40min后对RhB的矿化率可达81.1%,而未负载样品RhB矿化率仅为55.8%,表明Pt负载样品具有更优的降解速率和矿化能力.催化剂性能的提升归因于高能晶面暴露和Pt负载的协同作用.Pt纳米颗粒的负载作为助催化剂增加了催化剂表面的活性位点,同时提高了晶面光生电子空穴对的分离和迁移效率.     

海洋平台阴极保护与监测技术的应用

针对海洋平台全浸区的腐蚀情况,着重介绍了阴极保护及其原理、种类及特点,监测系统的组成及设计,探讨阴极保护及监测系统对海洋平台水下防腐的应用意义。     

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。     

Monitoring Air Quality in Mountains: Designing an Effective Network

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.