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.     

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。     

铜陵某富硫尾矿库周边土壤重金属污染特征及风险评价

铜陵某尾矿库堆积大量富硫尾矿,为研究其氧化状况及对周边土壤的污染情况,在尾矿库和周边采集尾矿及土壤样品,测试分析尾矿及土壤样品典型重金属(Hg、As、Cr、Cd、Pb、Zn)含量和赋存形态,并基于单因子污染指数法和内梅罗综合污染指数法对土壤污染状况进行了评价。结果表明:(1)尾矿中Fe_2O_3平均占比达到38.08%(质量分数,下同),S平均占比达到5.34%;(2)尾矿库周边土壤中Cd、As、Zn、Hg严重超标,分别为《土壤环境质量农用地土壤污染风险管控标准(试行)》(GB 15618—2018)风险筛选值的6.3、3.7、1.8、1.5倍;(3)土壤中重金属主要以残渣态形式存在,但Cd、Hg、Zn的非残渣态平均占比均达到25%以上,生物有效性较高,对周边环境存在潜在危害;(4)两种污染指数法评价结果表明,尾矿库周边土壤以Cd、As、Zn污染为主,该富硫尾矿库已对周边环境造成重度污染。     

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。     

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。     

含锌废物综合利用项目环境影响评价工程探索

工业生产的废物当中包含多种重金属,例如钢铁厂的粉尘当中就含有锌。含锌粉尘不仅会在高炉内循环和富集,影响高炉的运行,还会造成环境污染。基于此,本文主要分析含锌废物综合利用,对该项目进行环境评价,探讨有效的对策,以提高含锌废物综合利用的科学性,提高环境保护等级。