Research progress and hot spots of hydrothermal liquefaction for bio-oil production based on bibliometric analysis

Environmental Science and Pollution Research - Hydrothermal liquefaction (HTL) of biomass used HTL reaction under high temperature and pressure to produce bio-oil. This technology is considered as...     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

高燃速推进剂成型过程中的安全性分析

为研究高燃速推进剂成型过程中的安全性,根据配方组成和工艺条件,对其压伸过程的热点形成因素进行了理论分析,并采用热-结构耦合方法,选择有限元软件ABAQUS对其不同工艺条件下的压伸过程进行了建模分析。结果表明:在压伸过程中,物料在收缩段承受的压力最大,且温度变化显著。物料中残留的气泡是影响高燃速推进剂压伸成型过程安全性的主要因素。当物料中有残留气泡时,随压伸过程所加的载荷不同,气泡的最高温度会显著上升,载荷为15MPa时,气泡的最高温度可达139.3℃;载荷为20 MPa时,温度可达264.2℃。     

Determination of 27 pharmaceuticals and personal care products (PPCPs) in water: The benefit of isotope dilution

• Isotope dilution method was developed for the determination of 27 PPCPs in water. • The established method was successfully applied to different types of water samples. • The correction effect of corresponding 27 ILSs over 70 d was investigated. • Benefit of isotopic dilution method was illustrated for three examples. Pharmaceuticals and personal care products (PPCPs) are a unique group of emerging and non-persistent contaminants. In this study, 27 PPCPs in various water samples were extracted by solid phase extraction (SPE), and determined by isotope dilution method using liquid chromatography coupled to tandem triple quadruple mass spectrometer (LC-MS/MS). A total of 27 isotopically labeled standards (ILSs) were applied to correct the concentration of PPCPs in spiked ultrapure water, drinking water, river, effluent and influent sewage. The corrected recoveries were 73%–122% with the relative standard deviation (RSD)<16%, except for acetaminophen. The matrix effect for all kinds of water samples was<22% and the method quantitation limits (MQLs) were 0.45–8.6 ng/L. The developed method was successfully applied on environmental water samples. The SPE extracts of spiked ultrapure water, drinking water, river and wastewater effluent were stored for 70 days, and the ILSs-corrected recoveries of 27 PPCPs were obtained to evaluate the correction ability of ILSs in the presence of variety interferences. The recoveries of 27 PPCPs over 70 days were within the scope of 72%–140% with the recovery variation<37% in all cases. The isotope dilution method seems to be of benefit when the extract has to be stored for long time before the instrument analysis.     

饱和粉末活性炭电化学再生研究

采用电化学再生法对吸附处理染料废水产生的饱和粉末活性炭(PAC)进行再生,以NaCl为电解质,考察了电源类型、pH、再生时间、电压和NaCl浓度对活性炭再生率的影响,并对活性炭表面酸性含氧官能团进行测定。结果表明:(1)在pH=1、直流电压为6V、NaCl为6g/L、再生90min的最佳条件下,采用单室反应器,饱和PAC再生率达到59.09%。(2)电极循环伏安曲线结果表明,饱和PAC电极表面发生氧化反应,与主电极直接和间接氧化共同作用,使活性炭表面吸附的污染物降解。活性炭表面酸性含氧官能团滴定和红外光谱测试结果进一步表明,电化学再生法使活性炭表面基团得到恢复。     

射阳湖浮游动物群落结构特征及其与环境因子相关性研究

2016—2017年对射阳湖开展浮游动物群落结构和水质指标逐月监测。结果表明,射阳湖共鉴定出浮游动物61种,其中原生动物、轮虫、枝角类和桡足类分别为25种、24种、7种和5种。浮游动物种类数呈现春夏季多于秋冬季的变化趋势。浮游动物优势种数量较多、分布广,群落结构相对复杂且稳定。运用Shannon-Wiener指数、均匀度指数和B/T指数对射阳湖水质作评价,结果表明,该湖处于轻度—中度污染状态,生物学指标评价法与常规水质评价结果一致。浮游动物群落结构与水质指标相关性分析表明,水温、透明度、Chl-a和DO是影响射阳湖浮游动物群落结构的主要水质指标。     

喀斯特生态环境治理下土壤保持功能对石漠化的响应机制

喀斯特地区土壤保持功能是衡量石漠化生态治理成效的重要手段之一,基于InVEST模型,利用"3S"技术对2005、2010、2015年的关岭-贞丰花江示范区的石漠化进行解译,研究石漠化与土壤保持量两者的响应机制。结果表明:(1)2005～2015年石漠化程度总体减缓,面积由3 773.56 hm~2减少至3 474.23 hm~2,土壤保持总量相继上升,从29.29万t提高至37.29万t,2005～2010年单位面积土壤保持增量集中在6～30 t·hm~(-2)·a~(-1),后五年增量在0～6 t·hm~(-2)·a~(-1)。(2)由于土地利用方式、坡度、土层厚度等差异,土壤保持功能并未呈现简单式下降,而是表现为强度中度、轻度潜在、非喀斯特无石漠化。其中轻度与潜在差值最大,可达5.53 t·hm~(-2)·a~(-1),说明了潜在石漠化是今后治理的重点。(3)2005～2010年石漠化恢复型土壤保持增量为10.27 t·hm~(-2)·a~(-1),由于土地利用方式的转变以及治理时效性在2010～2015年降低为3.01 t·hm~(-2)·a~(-1)。稳定型石漠化变化土壤保持变化最为平稳,强度石漠化减缓区土壤保持功能上升快,轻度最为平缓,表明了治理期长且需循序渐进。因而,在治理过程中需考虑不同等级石漠化具有的土壤保持功能特性及其恢复的时效性。     

提高GPS定位精度的群调度滤波算法*

为解决现行安全生产科技领域中全球定位系统（GPS）/惯性导航系统（INS）融合系统容易产生较大时变误差的关键问题，满足GPS定位信号的精度和信号数据处理时间的要求，提出1种群调度滤波算法，通过实验仿真，分别与模糊算法、机器学习算法及卡尔曼滤波网络算法进行比较研究。结果表明:群调度滤波算法兼顾了定位精度与数据处理时间，具有较大优势，可在航空安全、船舶安全、应急监测、灾后抢险等安全科技领域广泛应用。     

地表水环境遥感监测关键技术与系统

本文主要是围绕地表水环境遥感检测展开讨论,全面介绍了水环境检测的关键技术和系统,建立改进双峰法的水体分布遥感提取方法,并以具体地区为例进行分析,采用不同方法建立解析方法,从根本上提升反演方法的区域和积极适用性,希望能够对相关人员起到参考性价值。