大气污染物垂直梯度自动监测系统的研制

设计的大气污染物垂直梯度自动监测系统是用 2个电磁阀实现 3个高度的气体采样 ,依靠分析仪器自身的抽气泵将气体样品通过聚四氟乙烯管抽入各分析仪器进行测量 ,并用一台微机和一块 AD/DA转换板实施对电磁阀的控制和数据采集 .现场观测实验表明 ,对于 25m长的采样管清洗时间可设置 1 min,而采样管长度间的差异不会导致测量 O₃、SO₂、NO和 NO₂ 小时平均浓度存在显著性差异 .     

厌氧-好氧法治理中药废水研究

厌氧—好氧法处理中药废水 ,污泥中生物相丰富 ,原生、后生动物数量多 ,污泥沉降性能优良 ,出水水质好 ,CODCr、BOD5 等指标都达到了一级排放标准。     

城市模化垃圾焚烧烟气中二氧化硫的生态特性

对模化广州市城市生活垃圾进行焚烧处理 ,从燃烧温度、炉内停留时间、过量空气系数以及垃圾尺寸等方面研究了 SO2 的生成特性和控制条件。     

Simazine biodegradation and community structures of ammonia-oxidizing microorganisms in bioaugmented soil: impact of ammonia and nitrate nitrogen sources

The objective of the present study was to investigate the impact of ammonia and nitrate nitrogen sources on simazine biodegradation by Arthrobacter sp. strain SD1 and the community structures of ammonia-oxidizing archaea (AOA) and bacteria (AOB) in non-agricultural soil. Soil microcosms with different treatments were constructed for herbicide biodegradation test. The relative abundance of the strain SD1 and the structures of AOA and AOB communities were assessed using quantitative PCR (q-PCR) and terminal restriction fragment length polymorphism (TRFLP), respectively. The co-existence of two inorganic nitrogen sources (ammonia and nitrate) had certain impact on simazine dissipation by the strain SD1. Bioaugmentation could induce a shift in the community structures of both AOA and AOB, but AOA were more responsive. Nitrogen application had significant impacts on AOA and AOB communities in bioaugmented soils. Moreover, in non-bioaugmented soil, the community structure of AOA, instead of AOB, could be quickly recovered after herbicide application. This study could add some new insights towards the impacts of nitrogen sources on s-triazine bioremediation and ammonia-oxidizing microorganisms in soil ecosystem.     

MCM-41介孔分子筛的合成及其对铜离子的吸附性能简

以微硅粉为硅源,CTAB和PEG-6000为模板剂,合成MCM-41介孔分子筛。采用XRD、N₂吸附-脱附曲线、FTIR以及TEM表征了其结构、比表面积、孔径分布及晶体形貌,并且以该样品为吸附剂,对含Cu²⁺的溶液进行了静态吸附实验。结果表明,以微硅粉为硅源成功合成了具有典型六方排列孔道结构的MCM-41,其比表面积为869.5 m²/g,孔容为0.97 cm³/g,平均孔径为3.3 nm;溶液pH为5~6时,MCM-41对Cu²⁺的去除效果最好;MCM-41对Cu²⁺的最大吸附吸附容量36.3 mg/g;MCM-41对Cu²⁺的吸附性能符合Langmuir吸附方程的特征。动力学研究表明,该过程符合准二级动力学模型。     

煤粉炉掺烧干化污泥的污染物排放研究

结合某电厂420 t/h四角煤粉炉掺烧污泥项目的实验室分析测试,了解煤粉掺烧不同含水率不同比例的干化污泥条件下烟气中污染物和灰渣中重金属的排放特性.结果表明,在实验研究配比和燃烧的条件下,大部分重金属元素Pb、Cu、Cr和Ni残留在灰渣中,Zn、Cd部分残留在灰渣中, As、Hg和Se等易挥发元素释放到烟气中,在灰渣中的含量很小.掺烧污泥后,灰渣中的重金属含量较单烧单煤都有了一定幅度的升高,Zn的含量是单煤的2倍,其余重金属是单煤的1.1~1.2倍.3种不同的掺混比例之间的污染气体排放浓度基本相似.烟气中主要污染物及重金属浓度可以满足现行国家标准.与单烧单煤相比,CO、HCl以及其他有机气体排放浓度基本相同;NH3的排放浓度较单煤有所升高;SO2、NOx和CO2排放浓度略有降低;飞灰浓度有所升高.烟气中的重金属,Hg含量升高了30%,Pb含量约为单煤的4.3~4.8倍.以上研究结果可为环保达标和飞灰利用提供基础数据.     

一株反硝化细菌的分离鉴定及其反硝化特性

从处理城镇污水的移动床生物膜反应器中分离获得一株反硝化细菌D3,并进一步研究该菌株的系统发育地位及反硝化特性。采用16S rDNA序列分析对菌株进行初步鉴定,探讨了基质浓度、起始pH和温度对菌株反硝化活性的影响。根据形态学特征、生理生化特性及16S rDNA序列测定分析,初步鉴定菌株属于寡养单胞菌属。该菌能利用硝酸钠或亚硝酸钠进行反硝化作用,最佳电子受体是硝酸盐氮,反硝化速率最大为19.86 mg/（L·h）,最适生长pH为7.36,最适生长温度为33.5℃。菌株D3在初始硝态氮浓度为140 mg/L,以乙酸钠为惟一碳源,pH为7.36,温度33.5℃的最优生长条件下,培养10 h进入对数生长期,倍增时间为9.9 h,48 h内硝酸盐还原率达95%。     

改性膨润土联合混凝剂处理微污染水中有机物

采用十二烷基多糖苷季铵盐作为改性剂制备改性膨润土,研究了改性膨润土联合混凝剂去除有机物的效果,考察了有机改性剂用量、微波辐射功率、辐射时间、膨润土投加量、pH值对有机物去除效果的影响,探讨了改性膨润土的改性和去除机理。结果表明,经微波改性后,实现了十二烷基多糖苷季铵盐阳离子对膨润土的插层,增大了层间距,提高了膨润土的吸附性能。联合混凝剂投加改性膨润土能够改善絮凝性能,显著提高有机物的去除效果。在最佳条件下,改性膨润土与PAC联合后的强化混凝,对初始浓度15.3 mg/L的微污染水中有机物的去除率达到95%以上,吸附符合Freundlich吸附等温方程。     

锰矿区土-水界面污染流中重金属来源分析

基于对湘潭锰矿红旗矿区土-水界面重金属污染流的采样分析,通过相关性和主成分分析法,研究了锰矿区土-水界面重金属污染流中的锰、镍、铜、锌、镉、铅重金属的来源以及同源性。结果表明:6种重金属的差异性、离散程度、变异性较大;锰—镍、锰—锌、锰—镉、锰—铅、镍—锌、铜—锌、铜—铅、锌—镉、锌—铅、铅—镉呈极显著正相关,镍与锰以外的其他重金属相关性都较低;3个主成分的贡献率分别为58.300%、16.628%、11.115%,累积贡献率达到了81.543%,并且6种重金属在主成分1的载荷非常高。结合矿区的周边环境和自身特点,表明6种重金属的主要共同来源为矿区内的矿业活动,并且工业活动、交通运输、农业活动等也对矿区造成了一定程度的重金属污染。     

Estimating and comparing greenhouse gas emissions with their uncertainties using different methods: A case study for an energy supply utility

Energy supply utilities release significant amounts of greenhouse gases (GHGs) into the atmosphere. It is essential to accurately estimate GHG emissions with their uncertainties, for reducing GHG emissions and mitigating climate change. GHG emissions can be calculated by an activity-based method (i.e., fuel consumption) and continuous emission measurement (CEM). In this study, GHG emissions such as CO₂, CH₄, and N₂O are estimated for a heat generation utility, which uses bituminous coal as fuel, by applying both the activity-based method and CEM. CO₂ emissions by the activity-based method are 12–19% less than that by the CEM, while N₂O and CH₄ emissions by the activity-based method are two orders of magnitude and 60% less than those by the CEM, respectively. Comparing GHG emissions (as CO₂ equivalent) from both methods, total GHG emissions by the activity-based methods are 12–27% lower than that by the CEM, as CO₂ and N₂O emissions are lower than those by the CEM. Results from uncertainty estimation show that uncertainties in the GHG emissions by the activity-based methods range from 3.4% to about 20%, from 67% to 900%, and from about 70% to about 200% for CO₂, N₂O, and CH₄, respectively, while uncertainties in the GHG emissions by the CEM range from 4% to 4.5%. For the activity-based methods, an uncertainty in the Intergovernmental Panel on Climate Change (IPCC) default net calorific value (NCV) is the major uncertainty contributor to CO₂ emissions, while an uncertainty in the IPCC default emission factor is the major uncertainty contributor to CH₄ and N₂O emissions. For the CEM, an uncertainty in volumetric flow measurement, especially for the distribution of the volumetric flow rate in a stack, is the major uncertainty contributor to all GHG emissions, while uncertainties in concentration measurements contribute a little to uncertainties in the GHG emissions.

Implications:Energy supply utilities contribute a significant portion of the global greenhouse gas (GHG) emissions. It is important to accurately estimate GHG emissions with their uncertainties for reducing GHG emissions and mitigating climate change. GHG emissions can be estimated by an activity-based method and by continuous emission measurement (CEM), yet little study has been done to calculate GHG emissions with uncertainty analysis. This study estimates GHG emissions and their uncertainties, and also identifies major uncertainty contributors for each method.