喀斯特地区非物质文化遗产空间格局及影响因素——以贵州省为例

厘清喀斯特地区非遗分布的空间格局有利于盘点资源现状,客观了解区域文化产生过程,以提高非遗的管理、保护与开发水平.基于地理学空间视角,利用GIS空间分析方法,探讨贵州9个地州市1023项非物质文化遗产类型结构、空间格局及影响因素,研究发现:(1)贵州省非物质文化遗产项目类型结构层次分明,民俗类数量最多,曲艺、传统医药与传统美术类相对稀缺;(2)贵州省非物质文化遗产空间分布不均衡,以贵阳市和黔东南州为双核心,中部和东部集聚明显,西部地区分布较少;(3)贵州省近90％的非物质文化遗产集中分布在海拔1400 m以下的喀斯特低山、中低山区域,曲艺类占比最高,传统体育、游艺与杂技和传统技艺两类分布与主要河流流域拟合较好,民间文学类与明清时期主要交通干线空间分布关联较高;(4)地形地貌、水系、文化、交通、经济等自然和人文因素对喀斯特地区非物质文化遗产空间格局产生重要影响.     

不同养殖模式池塘沉降颗粒营养物质组成特征

2012年4~10月采用自制沉降颗粒收集装置收集主养草鱼和黄颡鱼池塘生态系统中沉降颗粒物,开展了颗粒物质的垂直沉降量以及沉降颗粒中碳氮磷等营养物质的组成及沉降通量的季节变化特征的研究。结果表明:养殖可以显著提高池塘颗粒物质的垂直沉降量,主养草鱼池塘中颗粒物质垂直沉降量显著高于主养黄颡鱼池塘(p0.01),且两种养殖模式池塘颗粒物质沉降量随着养殖时间推进有显著增加的趋势。主养草鱼的池塘中颗粒物质垂直沉降量在100.39~414.66g/(m2·d)之间变化,平均为224.46g/(m2·d),主养黄颡鱼池塘中沉降颗粒物质垂直沉降量在34.14~272.91g/(m2·d)之间变化,平均为155.18g/(m2·d)。两种养殖模式沉降颗粒的碳氮磷成分在养殖周期内的变化规律不明显但具有相似的变化趋势,主养黄颡鱼池塘沉降颗粒中TN、TC、TOC和TON的含量均比同时期主养草鱼高,且均随着养殖时间的推进,沉降颗粒中碳氮磷的总量呈增加趋势。两种养殖模式池塘沉降颗粒中的C/N比值与沉积物中的C/N比值较为接近,表明沉降颗粒与沉积物营养物质来源具有一定的相似性,同时养殖系统内养殖对象与其所处环境的相互依存和相互影响对颗粒物质的产生以及沉降具有重要作用。     

Chlorinated and polycyclic aromatic hydrocarbons in riverine and estuarine sediments from Pearl River Delta, China

Spatial distribution of chlorinated hydrocarbons [chlorinated pesticides (CPs) and polychlorinated biphenyls (PCBs)] and polycyclic aromatic hydrocarbons (PAHs) was measured in riverine and estuarine sediment samples from Pearl River Delta, China, collected in 1997. Concentrations of CPs of the riverine sediment samples range from 12 to 158 ng/g, dry weight, while those of PCBs range from 11 to 486 ng/g. The CPs concentrations of the estuarine sediment samples are in the range 6-1658 ng/g, while concentrations of PCBs are in the range 10-339 ng/g. Total PAH concentration ranges from 1168 to 21,329 ng/g in the riverine sediment samples, whereas the PAH concentration ranges from 323 to 14,812 ng/g in the sediment samples of the Estuary. Sediment samples of the Zhujiang River and Macao harbor around the Estuary show the highest concentrations of CPs, PCBs, and PAHs. Possible factors affecting the distribution patterns are also discussed based on the usage history of the chemicals, hydrologic condition, and land erosion due to urbanization processes. The composition of PAHs is investigated and used to assess petrogenic, combustion and naturally derived PAHs of the sediment samples of the Pearl River Delta. In addition, the concentrations of a number of organic compounds of the Pearl River Delta samples indicate that sediments of the Zhujiang river and Macao harbor are most likely to pose biological impairment.     

铁矿石和生物炭添加对潜流人工湿地污水处理效果及温室气体排放的影响

人工湿地是温室气体的重要排放源,为了探索减少其温室气体排放的措施,通过在温室内构建了空白-人工湿地(湿地Ⅰ)、铁矿石-人工湿地(湿地Ⅱ)、生物炭-人工湿地(湿地Ⅲ)和铁矿石+生物炭-人工湿地(湿地Ⅳ)4组湿地,研究了铁矿石和生物炭基质的添加对潜流人工湿地污水处理效果和温室气体排放的影响.结果表明,4组湿地的平均出水CO...     

Fe3O4-TiO2Al吸附剂去除水中氟离子

以实验室制备的Fe3O4-TiO2·nH2O·Al吸附剂处理模拟和实际含氟废水,探讨了吸附剂用量、体系pH、吸附温度和吸附时间等因素对F-吸附效果的影响。结果表明:在初始F-浓度16.1 mg/L,起始pH 8.0,吸附剂投加量5 g/L,室温(约25 ℃)下吸附15 min时,模拟和实际废水的出水F-均可达到3O4-TiO2·nH2O·Al具有一定的实际应用价值。含氟水溶液初始pH对Fe3O4-TiO2·nH2O·Al吸附F-性能影响较大。在pH 介于3.0~5.0 时,吸附容量较大,过高或过低都会导致吸附容量降低。Fe3O4-TiO2·nH2O·Al吸附F-的过程为放热反应,升温不利于F-的吸附。该吸附剂吸附F- 的过程为化学吸附,符合准二级动力学模型,等温线拟合接近Freundlich吸附等温线。     

团花锦蛇、绿锦蛇和三索锦蛇的核型及Ag-NORs

报道了锦蛇属团花锦蛇（Ｅ．ｄａｖｉｄｉ）、绿锦蛇（Ｅ．ｐｒｖｓｉｎａ）和三索锦蛇（Ｅ．ｒａｄｉａｔａ）３个种的核型和Ａｇ－ＮＯＲｓ,团花锦蛇的型２ｎ＝３６（８Ｖ＋６ｓＶ＋２Ｉ＋２０Ｍ）,绿锦蛇的核型２ｎ＝３６（７Ｖ＋７ｓＶ＋２Ｉ＋２０ｍ）,三索锦蛇的核型２ｎ＝４０（６Ｖ＋３ｓＶ＋２ｓＩ＋５Ｉ＋２４ｍ）;①团花锦蛇和绿锦蛇的染色体数目和结构与此属大多数咱的相似,;②此种为雄性,未见染色体异型。③Ａｇ     

Geochemistry of tin (Sn) in Chinese coals

Environmental Geochemistry and Health - Based on 1625 data collected from the published literature, the geochemistry of tin (Sn) in Chinese coals, including the abundance, distribution, modes of...     

A performance evaluation of the National Air Quality Forecast Capability for the summer of 2007

This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC) that supported, in part, its transition into operational status. This evaluation focuses primarily on discrete forecasts for the maximum 8-h O₃ concentrations covering the 4-month period, June through September, 2007, using measurements obtained from EPA's AIRNow network. Results indicate that the 2007 NAQFC performed as well or better than previous configurations, despite the expansion of the forecast domain into the western half of the nation that is dominated by complex terrain. The mean, domain-wide, season-long correlation was 0.70. When examined over time, the domain-wide correlations exhibit a fairly consistent nature, with values exceeding 0.60 (0.70) over 90% (55%) of the days. The NAQFC systematically over-predicted the 8-h O₃ concentrations, continuing a trend established by earlier NAQFC configurations, though to a lesser degree. The summer-long mean forecast value of 53.2 ppb was 4.2 ppb higher than the observed value, resulting in a domain-wide Normalized Mean Bias (NMB) of 8.7%. Most of the over-prediction is associated with observed concentrations less than 50 ppb. In fact the model tends to under-predict when concentrations exceed 70 ppb. As with the bias, the error associated with the latest configuration was also lower. The summer-long Root Mean Square Error of 13.0 ppb (Normalized Mean Error (NME) = 20.4%) represented marked improvements over earlier forecasts. Examination of the spatial distribution of both the NMB and NME reveals that the NAQFC was generally within 25% for the NME and 25% for the NMB over a majority of the domain. Several areas of poorer performance, where the NMB and NME often exceed 25% and in some cases 50%, were noted. These areas include southern California, where the NAQFC tended to under-predict concentrations (especially on weekends) and the southeast Atlantic and Gulf coasts regions, where the model over-predicted. Subsequent analysis revealed that the incorrect temporal allocation of precursor emissions was likely the source of the under-prediction in southern California, while inaccurate simulation of PBL heights likely contributed to the over-prediction in the coastal regions.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.