高盐度采油废水生物处理影响因素

采用UASB+SBR法处理某海洋油田产生的采油废水。针对该废水难降解和高盐度的特性,探讨总盐度、Cl-浓度、处理负荷、曝气时间等因素对生物处理过程的影响,为工程设计及实际应用提供科学依据。     

强化水旋澄清池技术处理黄河高浊度水的试验研究

由于兰州段夏季黄河水的浊度较高,直接影响了自来水厂的正常运行,而目前水厂所用工艺无法满足高浊度水的处理要求,因此,采用自主研究开发的具有特殊结构的水旋澄清池对黄河高浊度水进行了强化处理的试验研究。研究结果表明：在多点投药混合方式下,强化水旋澄清池可提高浊度的处理效果;若在投加无机絮凝剂PAC后,再投加有机絮凝剂PAM进行复配作用,不仅可降低无机絮凝剂的用量,节约制水成本,而且提高了自来水厂的出水水质;结果也表明该水旋澄清池技术能够推广应用于黄河高浊度水的强化处理工艺中。     

钱塘江引水降氮示范工程的构建和运行研究

针对杭州西湖钱塘江引水低碳高氮的特点,提出以改性水草塘—复合垂直流人工湿地(IVCW)相耦合的生物—生态工艺进行引水处理,对该引水降氮示范工程的构建和运行效果进行了跟踪研究。结果表明,整个稳定运行期间(2012年7月19日至11月19日),耦合工艺对COD、TN、硝酸盐氮、TP的平均去除率分别为52.27%、52.49%、53.69%、52.79%,系统出水满足《地表水环境质量标准》(GB 3838—2002)Ⅳ标准;改性水草塘和IVCW单元作为耦合工艺的两个重要组成部分,在脱氮、除磷方面优势互补,从而共同保证出水水质的稳定;在改性水草塘单元,温度与COD的去除效果呈负相关关系,进水C/N与TN去除效果呈正相关关系;而在IVCW单元,温度与COD、TN的去除效果均呈线性正相关关系,进水C/N与两者的去除效果也呈正相关关系,且对COD的影响要大于对TN的影响。     

新型二氧化铅电极处理染料废水

利用高压塑片法制备掺杂镧和活性炭以及聚四氟乙烯(PTFE)的新型二氧化铅电极,采用X衍射(XRD),循环伏安曲线(CV),扫描电镜(SEM)等手段对电极性能进行表征,研究了其电催化处理有机染料废水(亚甲基蓝)的降解效果,并对反应机理进行了相关研究。研究表明,该电极具有良好的抗腐蚀能力,对亚甲基蓝具有较好的降解效果。通过与普通二氧化铅电极比较,该电极在染料脱色和去除COD方面都有较明显优势,显示出较好的工业应用前景。     

UV/Fenton技术处理某工厂高浓度乳化油废水

研究了UV/Fenton技术对高浓度金属清洗乳化油废水的处理效果,考察了亚铁与双氧水浓度、pH、反应时间和搅拌对COD去除效果的影响。实验结果表明,UV/Fenton技术对高浓度乳化油废水(COD平均浓度为35 000 mg/L)具有较高的去除效果,最佳工艺条件为:亚铁与双氧水浓度分别为2 400 mg/L和6 000 mg/L,pH为3,经过2 h反应,COD可降低至1 050 mg/L,去除率为97%。搅拌会降低COD的去除率。研究表明,UV/Fenton技术对高浓度乳化油废水具有很好的降解效果,且药品消耗较低,为目前此类高浓度有机废水的处理提供了技术参考。     

基于高频AQI数据的关中城市群空气污染规律探索

基于高频分时AQI及各污染物浓度数据,本文使用"AQI小时指数"、首要污染物等进行统计分析并建立VAR模型对关中城市群空气污染的总体情况、日内波动规律以及城市间空气污染的关联规律做了不同层次的挖掘。研究结果表明:(1)关中城市群的空气质量整体较差,春、冬季空气污染程度明显大于夏季,空气污染的"季节效应"和"集簇性"明显,且主要表现为颗粒物污染。(2)空气质量的日内波动规律在春、冬两季表现为下半天优于上半天,夏季夜间优于白天。但在不考虑四季AQI小时指数图"相位"差异的情况下,四季空气污染的日内波动呈现出明显的相似性。(3)各城市空气污染存在明显的关联规律。城市群内一个城市空气污染的恶化会加剧其他城市的空气污染,并且对其他城市空气污染的影响峰值会在24小时之内出现,且该影响会随着空间和时间尺度的增大而逐步衰减。     

气相色谱-高分辨双聚焦磁质谱测定水中超痕量多氯萘

采用固相萃取法处理水样，气相色谱-高分辨双聚焦磁质谱法测定水中超痕量多氯萘，同位素内标法定量，并对样品前处理条件和仪器条件进行优化。试验表明：方法在0．500 ng/L～500 ng/L范围内线性良好；当取样体积为1 L时，方法检出限为0．005 ng/L～0．01 ng/L；对实际水样进行2个质量浓度水平的加标回收试验，平行测定6次的 RSD为2．7％～8．7％，回收率为70．2％～110％。方法适用性试验表明，水样中复杂的基质对测定无影响。     

基于高通量测序的工业园区地下水和土壤细菌群落结构比较研究

为探究工业园区地下水和土壤细菌群落结构、多样性变化特征,采用高通量测序技术对地下水和土壤细菌16S r RNA基因高变区域进行序列测定。通过对Alpha多样性、物种组成、丰度和群落结构的分析,比较地下水和土壤细菌群落结构的异同。Alpha多样性的比较结果表明,土壤细菌群落多样性和丰富度明显高于地下水,地下水细菌群落多样性指数反映出地下水已受到周边污染源的影响。物种注释结果表明,地下水样品共检出48个细菌门,土壤样品共检出50个细菌门。变形菌门(Proteobacteria)、拟杆菌门(Bacteroidetes)和厚壁菌门(Firmicutes)是地下水细菌群落的优势类群,共占93.54%,且该工业园区地下水细菌群落呈现出典型的淡水种群特征;土壤中优势细菌门为Proteobacteria、放线菌门(Actinobacteria)、酸杆菌门(Acidobacteria)、Firmicutes和芽单胞菌门(Gemmatimonadetes),共占85.21%。由于地下水和土壤两者的生态系统和理化环境的差异,致使Actinobacteria、Acidobacteria、绿弯菌门(Chloroflexi)、硝化螺旋菌门(Nitrospirae)、α-变形菌纲(Alphaproteobacteria)、δ-变形菌纲(Deltaproteobacteria)和Gemmatimonadetes占比在地下水和土壤细菌群落间差异显著,同时使地下水和土壤细菌群落各含有一些特有的优势细菌属(地下水2个,土壤4个)。基于高通量测序技术对工业园区样品的测序结果可以为地下水和土壤环境的生态评价提供方法依据。     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Carbon dioxide emissions from cities in China based on high resolution emission gridded data

Based on the China high resolution emission gridded data (1 km spatial resolution), this article is aimed to create a Chinese city carbon dioxide (CO₂) emission data set using consolidated data sources as well as normalized and standardized data processing methods. Standard methods were used to calculate city CO₂ emissions, including scope 1 and scope 2. Cities with higher CO₂ emissions are mostly in north, northeast, and eastern coastal areas. Cities with lower CO₂ emissions are in the western region. Cites with higher CO₂ emissions are clustered in the Jing-Jin-Ji Region (such as Beijing, Tianjin, and Tangshan), and the Yangtze River Delta region (such as Shanghai and Suzhou). The city per capita CO₂ emission is larger in the north than the south. There are obvious aggregations of cities with high per capita CO₂ emission in the north. Four cities among the top 10 per capita emissions (Erdos, Wuhai, Shizuishan, and Yinchuan) cluster in the main coal production areas of northern China. This indicates the significant impact of coal resources endowment on city industry and CO₂ emissions. The majority (77%) of cities have annual CO₂ emissions below 50 million tons. The mean annual emission, among all cities, is 37 million tons. Emissions from service-based cities, which include the smallest number of cities, are the highest. Industrial cities are the largest category and the emission distribution from these cities is close to the normal distribution. Emissions and degree of dispersion, in the other cities (excluding industrial cities and service-based cities), are in the lowest level. Per capita CO₂ emissions in these cities are generally below 20 t/person (89%) with a mean value of 11 t/person. The distribution interval of per capita CO₂ emission within industrial cities is the largest among the three city categories. This indicates greater differences among per capita CO₂ emissions of industrial cities. The distribution interval of per capita CO₂ emission of other cities is the lowest, indicating smaller differences of per capita CO₂ emissions among this city category. Three policy suggestions are proposed: first, city CO₂ emission inventory data in China should be increased, especially for prefecture level cities. Second, city responsibility for emission reduction, and partitioning the national goal should be established, using a bottom-up approach based on specific CO₂ emission levels and potential for emission reductions in each city. Third, comparative and benchmarking research on city CO₂ emissions should be conducted, and a Top Runner system of city CO₂ emission reduction should be established.