软锰矿浆烟气脱硫动力学研究

研究了软锰矿浆烟气脱硫工艺SO2与软锰矿反应的动力学特征,考察了搅拌强度、温度和SO2浓度等因素对反应速率的影响,计算出反应活化能和反应级数,建立了揭示SO2与软锰矿反应动力学特征的锰浸出速率方程.     

Meteorological air pollution potential for Santiago,Chile: Towards an objective episode forecasting

The geography and climate of the Santiago basin are, in general, unfavorable for the diffusion of air pollutants. Consequently, extreme events occur frequently during the high pollution season extending from April to August. The meteorological conditions concurrent with those extreme events are mainly associated with the leading edges of coastal lows that bring down the base of the semipermanent temperature inversion reducing the dirunal growth of the surface mixed layer. In order to produce an objective 12 to 24-hour episode forecast, a two-way multivariate discriminant analysis has been used in the definition of a meteorological air-pollution potential index (MAPPI), separating high and low meteorological air-pollution potential days. The same procedure has been applied in the selection of the most efficient predictors for the MAPPI objective forecast, based on 12 and 24 UTC radiosonde data at Quintero, about 100 km to the NW of Santiago. Results indicate about 70% correctly forecasted days, with satisfactory skill-scores relative to persistency. The strong persistency characterizing the most efficient predictors in the 12-hour objective forecast scheme, makes the prediction of the first and last days of any particular air-pollution potential episode particularly difficult. To overcome this problem, a new set of predictors based on continuous measurements near the level of the top of the temperature inversion layer (900 hPa during air-pollution episodes) is being tested. Preliminary results indicate that the time-integrated zonal wind component at that level is a reliable precursor for both the onset and the end of air-pollution potential episodes.     

The role of automated feedback in training and retaining biological recorders for citizen science

The rapid rise of citizen science, with lay people forming often extensive biodiversity sensor networks, is seen as a solution to the mismatch between data demand and supply while simultaneously engaging citizens with environmental topics. However, citizen science recording schemes require careful consideration of how to motivate, train, and retain volunteers. We evaluated a novel computing science framework that allowed for the automated generation of feedback to citizen scientists using natural language generation (NLG) technology. We worked with a photo‐based citizen science program in which users also volunteer species identification aided by an online key. Feedback is provided after photo (and identification) submission and is aimed to improve volunteer species identification skills and to enhance volunteer experience and retention. To assess the utility of NLG feedback, we conducted two experiments with novices to assess short‐term (single session) and longer‐term (5 sessions in 2 months) learning, respectively. Participants identified a specimen in a series of photos. One group received only the correct answer after each identification, and the other group received the correct answer and NLG feedback explaining reasons for misidentification and highlighting key features that facilitate correct identification. We then developed an identification training tool with NLG feedback as part of the citizen science program BeeWatch and analyzed learning by users. Finally, we implemented NLG feedback in the live program and evaluated this by randomly allocating all BeeWatch users to treatment groups that received different types of feedback upon identification submission. After 6 months separate surveys were sent out to assess whether views on the citizen science program and its feedback differed among the groups. Identification accuracy and retention of novices were higher for those who received automated feedback than for those who received only confirmation of the correct identification without explanation. The value of NLG feedback in the live program, captured through questionnaires and evaluation of the online photo‐based training tool, likewise showed that the automated generation of informative feedback fostered learning and volunteer engagement and thus paves the way for productive and long‐lived citizen science projects.     

Scrap Iron Filings assisted nitrate and phosphate removal in low C/N waters using mixed microbial culture

• Microbes enhance denitrification under varying DO concentrations and SIF dosages. • Abiotic nitrate reduction rates are proportional to SIF age and dosage. • Over 80% of the simultaneously loaded NO₃⁻-N and PO₄³⁻ is removed biologically. This study focuses on identifying the factors under which mixed microbial seeds assist bio-chemical denitrification when Scrap Iron Filings (SIF) are used as electron donors and adsorbents in low C/N ratio waters. Batch studies were conducted in abiotic and biotic reactors containing fresh and aged SIF under different dissolved oxygen concentrations with NO₃⁻-N and/or PO₄^3- influent(s) and their nitrate/phosphate removal and by-product formations were studied. Batch reactors were seeded with a homogenized mixed microbial inoculum procured from natural sludges which were enriched over 6 months under denitrifying conditions in the presence of SIF. Results indicated that when influent containing 40 mg/L of NO₃⁻-N was treated with 5 g SIF, 79.9% nitrate reduction was observed in 8 days abiotically and 100% removal was accomplished in 20 days when the reactor was seeded. Both abiotic and seeded reactors removed more than 92% PO₄³⁻ under high DO conditions in 12 days. Abiotic and biochemical removal of NO₃⁻-N and abiotic removal of PO₄³⁻ were higher under independent NO₃⁻-N/PO₄³⁻ loading, while 99% PO₄^3- was removed biochemically under combined NO₃⁻-N and PO₄³⁻ loading. This study furthers the understandings of nitrate and phosphate removal in Zero Valent Iron (ZVI) assisted mixed microbial systems to encourage the application of SIF-supported bio-chemical processes in the simultaneous removals of these pollutants.     

环境内分泌干扰物--邻苯二甲酸酯的研究

介绍了环境内分泌干扰物的生殖毒性，综述了不同环境样品中邻苯二甲酸酯类内分泌干扰物的前处理技术和分析测试技术，并指出环境中邻苯二甲酸酯的污染状况。     

废印刷线路板回收利用的现状与存在的问题

从分析印刷线路板材料构成的复杂性人手,论述了国内外在废印刷线路板回收处理技术方面的研究现状和进展。找出目前我国在处理废印刷线路板方面存在的问题与差距．并提出切实可行的建议。分析表明,干法分离分选技术应是今后的发展方向。     

改性天然沸石去除水中氨氮的研究

分别采用NaCl、KCl和CaCl2对黑龙江省某市天然沸石进行改性，考察了pH值、氨氮初始浓度以及温度对改性沸石交换性能的影响，并对改性沸石的交换动力学进行了研究。结果表明，NaCl和KCl改性对沸石原矿交换容量有不同程度提高，而KCl改性后容量有所降低。pH、NH4初始浓度以及温度对交换性能有明显影响，pH6．0附近沸石交换容量最大；NH4初始浓度越高，反应速度越快，相同初始浓度下，钾型沸石交换速度较快，而沸石原矿交换速度最慢。采用Langmuir型离子交换等温线进行非线性回归的结果显示，低温有利于交换反应的进行。溶液中NH4^＋在改性沸石上的离子交换反应可采用Vermeulen模型描述，沸石原矿拟合相关系数较差。改性沸石多次再生后，其交换容量均有所降低。     

K2FeO4去除水体中的邻仲丁基4,6-二硝基苯酚

以水体中的邻仲丁基4,6-二硝基苯酚(DNBP)为研究对象,考察了K2FeO4在不同温度、pH和共存成分等条件下对其去除效果。结果表明,K2FeO4去除水体中DNBP所需的适宜pH为6.0~7.0,适宜温度在25~35℃,且K2FeO4与DNBP摩尔比大于20:1时,DNBP降解率大于90.0%。水体pH接近中性时,共存成分NH4Cl、NaCl、NaNO3和MnCl2对K2FeO4去除DNBP的影响很小。此外,K2FeO4与DNBP摩尔比较大时,DNBP的准一级动力学降解速率常数k与pH的关系符合Gauss模型。     

计算机技术在建筑火灾安全上的综合应用

为通过计算机技术低成本的、高效的、准确的解决建筑火灾安全问题,对计算机技术在建筑结构火灾安全上的综合应用进行研究,并以某一小学教学楼为例将应用成果进行展示。首先,通过FDS软件对该建筑某火灾场景进行了火场数值模拟。基于FDS的数据,利用RCFire程序进行了结构构件内部温度计算和结构火灾反应计算。最后,在Vega的虚拟现实平台上将构件内部温度和结构火灾反应的结果进行了动态图形展示,并实现了结构火灾反应的同步漫游。本文将建筑火场模拟技术、结构火灾反应计算技术和场景表现技术上综合应用到同一建筑上,形成了一个更为准确的结构火灾反应模拟系统,同时也为进一步的建筑火灾场景仿真奠定了基础。     

糖蜜酒精废水两相UASB处理有机物去除特征

应用气相色谱研究了糖蜜酒精废水两相ＵＡＳＢ处理工艺段中有机物的去除情况,结果表明,酸化反应器内,易降解有机物的去除率上为１００％,产甲烷段反应器内,因易降解化合物在酸化反应器内大部分被去除,剩下的化合物的在该段反应器内去除率偏小,仅为８０％。质谱结果证实,进水中的酚类化合物经两级处理后,被有效去除。