低温SNAD颗粒污泥工艺启动方式

为研究启动方式对同步短程硝化、厌氧氨氧化耦合反硝化(SNAD)颗粒污泥工艺的影响,低温(12.7~18.3℃)条件下,R1和R2反应器分别通过先启动全程自养脱氮(CANON)工艺和先启动厌氧氨氧化耦合反硝化(SAD)工艺的方式逐步启动SNAD颗粒污泥工艺.结果表明,R1反应器启动成功后,氨氮几乎完全去除,总氮去除率达到86.7%.低氨氮浓度运行时,出水总氮去除率下降至75.3%,出水总氮浓度在10 mg·L~(-1)左右,NOB存在过量增殖现象,出水总氮浓度超过北京市水污染物排放标准一级A规定.R2反应器启动成功后,出水几乎不含氨氮,总氮去除率在89.1%左右,略高于R1反应器.低氨氮浓度运行时,出水氨氮浓度小于1.0 mg·L~(-1),出水总氮浓度小于6 mg·L~(-1),出水氨氮和总氮浓度满足地标一级A标准.先启动SAD工艺可以在启动初期通过厌氧运行将NOB逐渐淘汰出系统内,维持了系统的稳定性,为后续曝气启动SNAD工艺提供了良好的基础,维持了反应器的稳定运行,实现出水总氮长期排放达标.     

低温高铁锰氨氮地下水生物同池净化

为实现低温(5~6℃)高铁锰氨氮[TFe 9.0~12.0 mg·L~(-1)、Fe(Ⅱ)6.5~8.0 mg·L~(-1),Mn(Ⅱ)1.9~2.1 mg·L~(-1),NH_4~+-N 1.4~1.7 mg·L~(-1)]地下水生物同池净化,以中试模拟滤柱在某水厂进行了实验研究.结果表明,出水总铁在启动之初即能合格,出水氨氮和锰分别在72 d和75 d实现净化.工艺启动周期受培养温度和原水水质影响较大.滤速越大,锰的极限去除量越低,滤速≥1.0 m·h~(-1)时,锰的极限去除量为3.0 mg·L~(-1).锰是滤速提升的限制因素,工艺极限滤速是4.5m·h~(-1).滤速≤6.0 m·h~(-1)时,氨氮的极限去除量为1.5 mg·L~(-1),且不受滤速影响,溶解氧(dissolved oxygen,DO)不足导致工艺对更高浓度氨氮净化失败.DO充足的条件下,工艺净化所需滤层厚度随锰和氨氮浓度增加而增厚.滤速增大会导致铁锰氨氧化去除区间向滤层深处位移,发生"锰"溶出现象.进一步分析表明,铁和氨氮在滤层内可同步氧化去除;锰的高效氧化去除区间与铁和氨氮的高效氧化去除区间存在明显分级.     

应用铁铜双金属有机骨架活化过硫酸盐催化剂去除水中有机污染物的研究

基于MIL-101(Fe)的合成方法,首次采用合成前金属掺杂法制备了双金属有机骨架MIL-101(Fe,Cu)催化剂,并利用X射线衍射仪(XRD)、傅里叶变换红外光谱仪(FTIR)、扫描电镜(SEM)和X射线能谱仪(EDS)对催化剂进行表征.同时,以罗丹明B(Rh B)为目标降解物,研究此催化剂催化过硫酸盐降解Rh B的性能,并初步尝试将催化剂应用于邻苯二甲酸二丁酯(DBP)的催化降解.XRD、FTIR、SEM和EDS等分析结果表明,铜离子的掺杂导致MIL-101(Fe)的结构与形貌发生了改变,提高了对污染物的吸附和催化降解作用.相比MIL-101(Fe),在90 min时MIL-101(Fe,Cu)对Rh B的去除率达到95%,在180 min时对DBP的去除率达到68%.此外,分析了催化剂催化性能提高的原因:配位竞争导致铁离子的不饱和趋向;形貌的变化加强了对污染物的吸附作用.     

Challenges to quantitative applications of Landsat observations for the urban thermal environment

Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program sig-nificantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature (LST). However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM + and channel differences among sensors. Based on these investigations, the concerns are to: (1) show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors; (2) emphasize efforts which should be done for the quantitative applications of Landsat data; and (3) understand the potential challenges for the continuity of Landsat observation (i.e., thermal infrared) for global change monitoring, while several climate data record programs being in progress.     

Improved activity and durability of Rh-based three-way catalyst under diverse aging atmospheres by ZrO2 support

The catalytic activity and durability of Rh/ZrO_2 catalyst were investigated compared with Rh/Al_2O_3 catalyst under diverse aging atmospheres, including lean, rich and lean–rich cyclic aging atmospheres, to simulate the real working conditions of three-way catalyst.Oxidation states and microstructures of rhodium species were investigated to correlate with the catalytic performance of the catalysts. The catalytic performance and durability of the Rh catalyst under diverse aging atmospheres were drastically enhanced by ZrO_2 support. ZrO_2 support was confirmed to be able to effectively inhibit rhodium sintering even under diverse aging conditions. It can also successfully keep Rh species in an active low-valence state on the surface of the catalyst. The superiority of ZrO_2 support compared to Al_2O_3 was verified by the Rh-based monolith catalyst.     

基于水足迹强度的江苏省水资源利用效率分析

水足迹强度是可以量化水资源利用效率的指标,文章在计算江苏省水足迹的基础上,选取6项水足迹强度指标,分部门比较各地市水资源利用效率的差异,并基于ESDA研究水足迹强度的空间自相关性,构建空间集聚图。研究表明:2002~2014年江苏省各市水足迹总体呈现上升趋势,水资源压力增大;总体上苏南和苏北地区的农业水足迹强度都偏高,农业水资源利用效率低;苏南地区工业产值高但耗水量大,因此工业水足迹强度也偏高;苏北地区的灰水足迹强度偏高,水资源压力较大;江苏省水足迹强度具有不断增强的全局自相关性,地市出现集聚现象,不同地市的集聚分布也日益明显;大部分苏北地区属于高高集聚并且向外扩张,低低集聚主要分布在苏南地区。研究结果为政府部门分析地市间水资源利用效率差异,制定高效的水资源利用政策等提供依据。     

生态环境空间分级管控策略研究

生态环境空间分级管控是为了确保各级尺度下的生态环境空间充分发挥其生态服务功能所采取的管理控制手段与途径,同时给城市生态环境空间的管控提供依据,在不同尺度下,生态环境空间需按照不同精细度的管控方法和不同的土地空间单位进行分级,生态环境空间分级管控主要包括:省域尺度生态环境空间的管控、市域尺度生态环境空间管控和在主城区尺度下生态环境空间管控。通过3种尺度不同管控方法和策略的有效衔接,从宏观、中观和微观3个层次保证了生态规划的完整性和连续性,使生态系统服务真正发挥其价值。     

洱海入湖河口湿地干湿季沉积物氮、磷、有机质垂向分布特征及污染风险差异性

以云南洱海罗时江河口湿地为典型对象,利用柱状底泥分层采样器采集罗时江河口湿地表层(0~10 cm)、中层(10~30 cm)和底层(30~60 cm)沉积物样品,分析干、湿季沉积物总氮(TN)、总磷(TP)、有机质(OM)垂向分布特征,并对沉积物进行污染风险评价.结果表明:1干季罗时江河口湿地表层、中层和底层沉积物TN平均含量分别为1.734、1.453和1.255g·kg~(-1),TP平均值分别1.085、1.034和0.992 g·kg~(-1),OM平均值分别为59.051、47.730和42.133 g·kg~(-1);湿季罗时江河口湿地表层、中层和底层沉积物TN平均含量分别为1.147、0.948和0.895 g·kg~(-1),TP平均值分别0.599、0.523和0.519g·kg~(-1),OM平均值分别为53.098、46.897和43.395 g·kg~(-1);干、湿季各指标含量垂向分布随沉积深度的增加呈下降趋势,表层富集明显;三层沉积物各指标含量均为干季高于湿季,且除OM外,TN和TP均达到显著差异(P0.01).2单因子指数(PI)、有机氮指数(ON)和有机指数(OI)评价结果表明,整体上干季沉积物氮磷属于重度污染,有机质属于中度污染;湿季沉积物氮和有机质均为中度污染,磷为轻度污染;总体上沉积物氮、磷、有机质污染水平为干季高于湿季,且干、湿季各污染指数均为表层最高,中层次之,底层最低,表层污染最为严重.3干、湿季罗时江河口湿地污染来源存在差异,干季以外源为主,湿季以内源为主;干季沉积物营养盐潜在释放风险较湿季大,且以表层释放风险最大.     

溶解氧对碳氮硫共脱除工艺中微生物群落影响解析

为了研究溶解氧对碳氮硫共脱除工艺中微生物群落的影响,探索供氧条件下提高单质硫转化率的机制,实验采用基因芯片GeoChip技术来解析不同曝气量下采集的污泥样品中的微生物群落结构特征.研究结果表明,微量的溶解氧能显著影响工艺中的微生物群落结构(P<0.001),相对较高浓度的溶解氧可以增加微生物的多样性但是却抑制了对溶解氧比较敏感的反硝化微生物的活性,导致工艺硝酸盐去除率下降.与反硝化微生物相比硫酸盐还原菌(sulfate-reducing bacteria,SRB)能抵抗更高浓度的溶解氧,因此工艺的硫酸盐还原过程未受到影响,一直保持较高的硫酸盐去除率.当曝气量为20 mL·min-1时单质硫的转化率最高,通过对sox(sulfur-oxidation)基因丰度分析和聚类分析发现,在该曝气量条件下sox基因的丰度最高,与厌氧条件相比有极其显著的差异.此外,GeoChip还检测到一些硫氧化细菌(sulfur-oxidizing bacteria,SOB)的丰度明显提高,证明适量的溶解氧能够刺激一部分SOB的生长与代谢.     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.