高温高压下煤孔隙结构的变化对瓦斯吸附特性的影响

为了研究高温高压条件下煤孔隙结构变化对瓦斯吸附特性的影响,选取九里山矿无烟煤,在压力为7 MPa、温度为40～130℃的条件下进行等温吸附实验和压汞实验。研究结果表明:煤样对甲烷的等温吸附曲线在该压力、温度条件下符合Ⅰ型吸附曲线特性,吸附规律符合Langmuir吸附模型;在压力7 MPa和温度130℃条件下,煤样的孔隙结构发生一定的变化,煤的比表面积增大、累计孔体积降低,可见孔及裂隙的数量比例增高,加强了煤样孔隙之间的连通度,导致原本吸附在煤样表面的甲烷分子大量解吸;在压力不变的情况下,随着温度的不断增高,煤的极限吸附量逐渐减小,其主要原因是样品孔隙结构的破坏和分子间作用力的变化。     

高含盐废水脱盐处理技术研究进展

综述了几种常见的高含盐废水脱盐处理技术的发展历程、工艺原理、优缺点及目前的研究进展,分析了热分离、膜分离、电渗析、离子交换、电吸附、微生物脱盐等方法的优缺点,展望了未来废水脱盐工艺的发展方向。指出：脱盐方法将根据各类水体的水质特点更加精细化;多种脱盐技术联合应用也是今后废水脱盐的发展方向。     

SMBBR-AF-SMBBR组合工艺处理高氨氮农药废水

采用特异性移动床生物膜反应器(SMBBR)和厌氧生物滤池(AF)组合工艺处理高氨氮农药废水。考察了HRT、pH和DO等工艺条件对SMBBR-AF-SMBBR组合工艺运行稳定期COD和氨氮去除率的影响。试验结果表明,在进水COD为2 408~7 440 mg/L、ρ(NH_4~+-N)为160.21~433.84 mg/L、TN为208.27~537.65 mg/L、HRT为8d、pH为8.0、DO为4 mg/L的条件下,处理后出水平均COD为342 mg/L,COD去除率达92.3%;ρ(NH_4~+-N)小于4.0mg/L,氨氮平均去除率为89.2%;TN小于50 mg/L,平均TN去除达83.0%。出水各指标均优于原A2O工艺出水。     

火焰脉动在火灾领域相关研究进展

为研究火焰脉动在火灾领域相关研究进展,总结了不同燃料类型的火焰脉动形成机理,较为详细地介绍了火焰脉动现象研究中常用的脉动频率测量方法,列举了LDV、TDLAS、自由基团高频采集等新方法;介绍了池火脉动、射流/羽流气体火脉动在不同燃烧器尺寸,不同外部环境和不同燃料比等影响因素方面的研究内容与进展;对常见的3种不同的火焰脉动模型进行描述和归类;对火焰脉动的规律、机理等在火灾领域的早期识别和检测中的作用、特殊条件对于火焰脉动频率的影响和更加精细的火焰脉动模型的研究等方向提出展望。     

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。     

催化裂解法处理有机硅高沸物

采用催化裂解法处理有机硅高沸物,解决高沸物储存过程中的安全、环保问题。研究了催化剂种类、HCl加入量和精馏高沸物加入量对高沸物转化率的影响。实验结果表明:在以N,N-二丁基-1-丁胺为催化剂、合成高沸物为原料、HCl加入量为13%(w)的条件下,高沸物转化率为83.10%,二甲基二氯硅烷收率为23.78%;合成高沸物中精馏高沸物加入量为9.0%(w)时,高沸物转化率最高,为85.58%。     

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。     

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.     

基于内容分析法的土地利用总体规划目标偏好研究

土地利用总体规划需要统筹区域发展多元目标以增强对区域发展的指导力和约束性,因此全面了解我国土地利用总体规划的政策目标偏好,能为优化规划技术方法和改革规划管理体制提供政策参考。鉴于此,本文在分析我国土地利用总体规划多元目标及其关系的基础上,采用内容分析法基于全国土地利用总体规划纲要进行词频挖掘构建目标偏好测量维度及体系,进而对两轮省级土地利用总体规划文本进行目标偏好的定量测度,结果表明:(1)土地利用总体规划是多目标规划,通过内容分析法可以构建由经济、社会、生态三大目标构成的目标偏好测量体系;(2)从目标偏好排序来看,三大目标中社会目标始终是两轮土地利用总体规划的首要偏好目标,生态目标是土地利用总体规划的次要偏好目标,经济目标排在最后;同时相比上轮规划,本轮规划耕地保护子目标次序提前,土地整治子目标次序推后;(3)从目标偏好程度来看,社会目标的首位偏好程度有所下降,但其中耕地保护子目标的首位偏好度明显上升;生态目标和经济目标的首位偏好程度基本不变,总体而言本轮规划三大目标偏好程度的差异缩小。研究结果说明,我国土地利用总体规划始终坚持以耕地保护为核心的社会目标偏好,符合其是国土资源管理部门主导的部门规划的定位;虽然土地利用总体规划对经济、社会、生态三大目标的偏好程度差异缩小,但其若要在空间规划体系中发挥更大的作用,还需要通过优化规划技术和改革规划管理体制进一步均衡经济、社会、生态目标,以承担起统领全区域空间发展的要求。     

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.