湘江铜霞段镉环境容量模型研究

镉是一种毒性很强的累积性重金属污染物,在<污水综合排放标准>中是第一类污染物.为了控制湘江铜霞段的镉污染,根据该段水环境功能要求及水力学特征,在分析多年水文参数和相应环境监测资料的基础上,根据质量守恒定律,应用稳态二维模型建立了该江段镉环境容量模型.经用该段2002年的3月、6月、9月和12月份的有关监测数据检验,结果表明,模型预测值与实测值相对误差为1.38%～21.4%,具有较好的相关性.该模型可用于该江段镉环境容量及水体中镉浓度时空分布的预测预报,对强化该江段环境管理、实施区域含镉废水污染源总量控制具有指导意义.     

基于文献计量分析的珊瑚礁生态修复技术研究现状及发展趋势

珊瑚礁是海洋生态系统的重要组成部分,具有食物资源、药用价值、海岸防护等生态功能.近年来受到自然气候及人类活动的影响,珊瑚礁生态系统发生了大规模的退化,因此,珊瑚礁生态修复技术逐渐成为研究热点.本文从Web of Science核心合集、CNKI、德温特数据库等获得相关文献,对全球及国内珊瑚礁生态修复技术研究热点进行全面...     

Effects of climate and land-cover change on the conservation status of gibbons

Species shift their distribution in response to climate and land-cover change, which may result in a spatial mismatch between currently protected areas (PAs) and priority conservation areas (PCAs). We examined the effects of climate and land-cover change on potential range of gibbons and sought to identify PCAs that would conserve them effectively. We collected global gibbon occurrence points and modeled (ecological niche model) their current and potential 2050s ranges under climate-change and different land-cover-change scenarios. We examined change in range and PA coverage between the current and future ranges of each gibbon species. We applied spatial conservation prioritization to identify the top 30% PCAs for each species. We then determined how much of the PCAs are conserved in each country within the global range of gibbons. On average, 31% (SD 22) of each species’ current range was covered in PAs. PA coverage of the current range of 9 species was <30%. Nine species lost on average 46% (SD 29) of their potential range due to climate change. Under climate-change with an optimistic land-cover-change scenario (B1), 12 species lost 39% (SD 28) of their range. In a pessimistic land-cover-change scenario (A2), 15 species lost 36% (SD 28) of their range. Five species lost significantly more range under the A2 scenario than the B1 scenario (p = 0.01, SD 0.01), suggesting that gibbons will benefit from effective management of land cover. PA coverage of future range was <30% for 11 species. On average, 32% (SD 25) of PCAs were covered by PAs. Indonesia contained more species and PCAs and thus has the greatest responsibility for gibbon conservation. Indonesia, India, and Myanmar need to expand their PAs to fulfill their responsibility to gibbon conservation. Our results provide a baseline for global gibbon conservation, particularly for countries lacking gibbon research capacity.     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

化学吸收法采集固定污染源氨气的影响因素探究

固定污染源氨气的手工监测,干扰因素较多,其中采样环节尤为关键。实验通过催化氧化-化学发光法考察不同采样管线材质对氨气的吸附效果,离子色谱法考察化学吸收法采集氨气的吸收瓶类型、采样流量、吸收液种类、吸收液浓度及体积等采样条件对氨气吸收效率的影响。实验表明,316 L不锈钢与聚四氟乙烯对氨气的吸附较小,氨气的采样流量不宜超过1.0 L/min,棕色气泡式吸收瓶更适用于氨气样品的采集,磷酸溶液作吸收液对氨气的吸收效率较好,对于低浓度的氨气样品,应采用低流量长时间采集。     

江苏省宿迁市某监测井地下水砷超标原因研究

江苏宿迁市中部某远离工业企业的监测井地下水砷（As）指标连续多期次超《地下水质量标准》（GB/T 14848—2017）Ⅳ类标准限值。为探究该监测井地下水As长期超标原因,通过在监测井附近新钻取岩芯沉积物中不同形态As的提取实验,结合新井和监测井中地下水的同步取样与水质参数的测试,系统分析了监测井点位地下水As超标的原因。研究结果发现,离监测井5 m处的新井地下水As质量浓度不超标,而监测井中As仍超标。对比新井和监测井的水质参数与沉积物中As的赋存形态发现,在还原性和弱碱性的环境条件下,地下水类型及地层沉积物中碳酸盐结合态的变化可能对地下水As质量分数有明显的影响,而铁-锰氧化物结合态与有机物-硫化物结合态的变化对As质量分数超标影响则有限。此外,不排除可能受到成井工艺不规范与监测井受到串层污染的影响。     

腐植质与有机污染物作用研究进展

综述了腐殖质的结构特性、与有机污染物的结合性质以及腐殖质对有机污染物毒性和生物可利用率的影响。已有的研究表明:腐殖质与有机污染物的结合能力受到下列因素的影响———腐殖质的结构、腐殖质的分子量和浓度、有机物的结构、水环境条件以及光化学降解作用等;腐殖质的存在通常降低了有机污染物的毒性,但在少数情况下例外,其推测性的机理还有待于实验进一步验证;有机污染物的生物可利用率不仅与腐殖质的浓度和组成有关,还受水体PH值的影响。本文同时对腐殖质的结构和多样性的研究、腐殖质对极性有机污染物的迁移、毒性和生物可利用率的研究、腐殖质增强某些有机物毒性的机理的研究进行了展望。     

浙江省沿海地区农村房屋抗风情况调研

对浙江省沿海地区的农村房屋抗风情况进行了调研,总结了被破坏房屋的主要结构特点。在对现有房屋的统计中发现相对不富裕地区高达60%以上的房屋属于抗风能力差和较差的,而一些比较富裕且受过台风袭击有过惨痛教训地区的房屋抗风能力差和较差的仅在20%以下。进一步研究发现抗风能力较好的房屋大都是建造年代不久,多为混凝土现浇板屋盖,上部多为框架或砖混结构,而抗风能力较差和差的大都为建造年代已久,多为小青瓦屋面,上部结构多为砖木结构或构造措施较差的砖混结构。同时对当地村民传统的"砖头压瓦"防护措施进行理论验算,得出当风速超过台风界限风速时,屋面上的砖头将会飞起,该措施应该避免。     

结构应力逆向推演模型及其不确定度定量分析

目的 针对现有结构安全在线监测与评估方法存在的短板,结合人工智能方法探索新的解决方案。提出一种基于有限测点应力逆向推演整体结构应力分布的方法,以数据驱动的模式,基于神经网络技术搭建算法模型。方法 以结构有限元仿真数据为基础,运用相关性分析方法,获取代表结构响应特征的有限个测点,利用神经网络方法构建基于有限测点应力推演结构全场应力分布的算法模型。结果 以科学试验平台的连接器结构为对象,开展该算法模型的应用研究,并且对该应用实例下的算法模型开展不确定度分析,推演结果的相对不确定度u95rel为8.6%。结论 该算法模型的推演结果正确反映了结构总体响应特征。从建模过程角度分析,该算法模型的不确定度来源主要包括相关性分析方法、神经网络建模以及模型收敛条件3个方面。     

Insight into the dynamic microbial community and core bacteria in composting from different sources by advanced bioinformatics methods

Microbial communities are important for high composting efficiency and good quality composts. This study was conducted to compare the changes of physicochemical and bacterial characteristics in composting from different raw materials, including chicken manure (CM), duck manure (DM), sheep manure (SM), food waste (FW), and vegetable waste (VW). The role and interactions of core bacteria and their contribution to maturity in diverse composts were analyzed by advanced bioinformatics methods combined sequencing with co-occurrence network and structural equation modeling (SEM). Results indicated that there were obviously different bacterial composition and diversity in composting from diverse sources. FW had a low pH and different physiochemical characteristics compared to other composts but they all achieved similar maturity products. Redundancy analysis suggested total organic carbon, phosphorus, and temperature governed the composition of microbial species but key factors were different in diverse composts. Network analysis showed completely different interactions of core bacterial community from diverse composts but Thermobifida was the ubiquitous core bacteria in composting bacterial network. Sphaerobacter and Lactobacillus as core genus were presented in the starting mesophilic and thermophilic phases of composting from manure (CM, DM, SM) and municipal solid waste (FW, VW), respectively. SEM indicated core bacteria had the positive, direct, and the biggest (>?80%) effects on composting maturity. Therefore, this study presents theoretical basis to identify and enhance the core bacteria for improving full-scale composting efficiency facing more and more organic wastes.