降水量时间序列变化的小波特征

利用小波变换对降水量时间序列的多时间尺度变化及突变特征进行了探讨。小波变换不仅能将降水量时间序列的频率特征在时间域上展现出来，清晰地给出各种时间尺度的强弱和分布情况以及早涝变化趋势和突变点，而且还能分析出其主要周期。以新安江流域黄山地区主汛期(5—7月)和年降水量为例，计算表明，其年际及年代际时间尺度在时域中分布不均匀，具有明显的局部化特征；同时分析出主汛期降水具有8年、19年左右的周期，年降水存在6年、19年左右的周期；研究还表明，主汛期降水与年降水的时间尺度变化比较接近。     

鄱阳湖流域水文效应对气候变化的响应

以鄱阳湖流域为研究区,以地表 地下耦合的分布式水文模型WATLAC为模拟工具,探讨流域水资源对气候变化的响应。水文模型以2000～2008年为模拟期,以流域河道日径流量来率定（2000～2005年）与验证模型（2006～2008年）并取得了满意的模拟效果。基于此,假定未来气候变化情景方案,通过径流量、土壤蒸发量和基流量来探讨气候变化对流域水资源的影响。结果表明,径流量与基流量对降雨变化有着较强的敏感性,而土壤蒸发量对温度变化的敏感性较强。在降雨一定条件下,水文变量均与气温变化近似呈线性关系;在气温情景一定条件下,水文变量均与降雨变化呈非线性关系。随着降水的减少,气温对径流、土壤蒸发和基流的影响也随之减弱;气温对上述变量的显著影响主要表现在降水增加的情况下。相同的气温变化情景下,降水增加比降水减少对径流量的影响更加显著,降水减少比降水增加对土壤蒸发量与基流量的影响更加显著,表明降水变化对水文变量有着不同程度和方向的影响作用     

岷江上游生态脆弱性评价

岷江上游流域是我国典型的生态脆弱区之一,由于地质变化频繁、高差显著、气候干旱,加上人为活动影响,生态脆弱性的表现十分明显。通过对其生态环境脆弱性因素及成因机制的分析,构建了由土地生产力、地表起伏度、干燥度指数、土壤侵蚀强度、草场退化荒漠化率、物种消失率等14个指标组成的岷江上游生态脆弱性的评价指标体系;根据本地区生态环境现状、全国和四川省情况及奋斗目标,建立了Ⅰ到Ⅲ级的评价标准体系;利用模糊数学聚类方法对评价指标进行分析计算,得出了岷江上游生态环境为第Ⅲ级,即生态环境非常脆弱的结论。评价结果符合岷江上游地区的生态环境状况。     

地域系统研究进展与展望

在述评国内外地域系统研究的基础上．分析了目前在“生态”框架下进行综合地域系统研究中存在的问题。①生态区是一个开放系统。任何尺度下的生态区都是由不同的生态系统组成。因此生态区的同质性是相对的，导致区划单元界线划定存在分歧。②受基础数据资料精度的限制．指标选取较多考虑是否能够满足数理统计需要而在一定程度上忽视了生态学上的意义和作用。③人类活动在生态区中既受到自然因子的制约．也对自然因子产生偏移，区域社会经济发展模式是自然、人文因子综合作用的结果．因而生态区包含自然因子和人文因子的信息。文中指出掌握自然-人文因子相互作用机制实现自然-人文因子的拟合是综合地域系统研究的核心．运用自然-人文因子空间化技术进行多源数据的融合是划分综合地域系统必须解决的关键技术．同时对将自然-人文空间化技术手段应用于综合地域系统划分中需要解决的问题进行了探讨。     

基于百度指数的长江三角洲虚拟旅游流流动特征和影响因素分析

旅游流是游客在空间区域内的集散过程所带来的一系列经济社会发展效应,是旅游系统的神经中枢和纽带.传统旅游流研究多集中于现实旅游流,对虚拟旅游流涉及较少,数据获取多依靠调查问卷与景区提供,具有局限性.文章基于百度指数,在保证数据容量与准确度的基础上以长江三角洲为研究对象,构建O-D虚拟旅游流矩阵探究区域虚拟旅游流流动特征,并利用地理加权回归(GWR)模型从流入市域、流出市域两方面,分析不同市域的虚拟旅游流影响因素.结果 表明:(1)长江三角洲虚拟旅游流空间分布不均,流动性较强,形成以合肥、南京、苏州、杭州、上海为核心节点的放射状网络.(2)A级景区数量、住宿和餐饮业从业人员以及公路里程可以较好的解释流入虚拟旅游流市域分布.其中,A级景区对流入虚拟旅游流影响最为显著,住宿和餐饮业的发展对长江三角洲西北部虚拟旅游流的流入具有推动力,而公路里程对整个长江三角洲流入虚拟旅游流影响最小.(3)私人汽车拥有量、社会消费品零售总额、互联网宽带用户接入量对流出虚拟旅游流有较强的影响.社会消费品零售总额代表的经济因素是市域虚拟旅游流流出的决定因素,经济越发达的区域,人们的出游意愿越强烈,也更愿意利用网络抓取旅游信息流追求高质量旅游出行行为.     

基于BP神经网络的鄱阳湖水位模拟

考虑到鄱阳湖水位受流域五河与长江来水等多因素的共同作用而表现出高度非线性响应,采用典型的三层BPNN神经网络模型来模拟鄱阳湖水位与其主控因子之间的响应关系。分别将湖口、星子、都昌、棠荫和康山水位作为目标变量进行BPNN模型构建和适用性评估。结果显示：综合考虑流域五河及长江来水（汉口或九江）的BPNN水位模型,空间站点水位模拟精度（R2和Ens）可达090以上,各站点的均方根误差（RMSE）变化范围约050～10 m,若忽略长江来水的影响作用,仅将流域五河来水作为湖泊水位的主控影响因子,模型训练期与测试期的纳希效率系数（Ens）和确定性系数（R2）显著降低,且低于050,均方根误差（RMSE）也明显增大（124～288 m）,意味着综合考虑流域五河与长江来水是获取结构合理、精度保证的鄱阳湖水位模型的重要前提。同时建议针对鄱阳湖湖盆变化对水位的影响,尽可能选择一致性较好的长序列数据集来训练和测试BPNN模型。所构建的BPNN神经网络模型可进一步结合流域水文模型,用来预测气候变化与人类活动下流域径流变化对湖泊水位的潜在影响,也可作为一种有效的模型工具来回答当前鄱阳湖一些备受关注的热点问题,如定量区分流域五河与长江来水对湖泊洪枯水位的贡献分量,为湖泊洪涝灾害的防治和对策制定提供科学依据     

Increasing the recovery of heavy metal ions using two microbial fuel cells operating in parallel with no power output

The present study aimed to improve the performance of microbial fuel cells (MFCs) by using an intermittent connection period without power output. Connecting two MFCs in parallel improved the voltage output of both MFCs until the voltage stabilized. Electric energy was accumulated in two MFCs containing heavy metal ions copper, zinc, and cadmium as electron acceptors by connection in parallel for several hours. The system was then switched to discharge mode with single MFCs with a 1000-Ω resistor connected between anode and cathode. This method successfully achieved highly efficient removal of heavy metal ions. Even when the anolyte was run in sequencing batch mode, the insufficient voltage and power needed to recover heavy metals from the cathode of MFCs can be complemented by the developed method. The average removal ratio of heavy metal ions in sequencing batch mode was 67 % after 10 h. When the discharge time was 20 h, the removal ratios of zinc, copper, and cadmium were 91.5, 86.7, and 83.57 %, respectively; the average removal ratio of these ions after 20 h was only 52.1 % for the control group. Therefore, the average removal efficiency of heavy metal ions increased by 1.75 times using the electrons stored from the bacteria under the open-circuit conditions in parallel mode. Electrochemical impedance data showed that the anode had lower solution resistance and polarization resistance in the parallel stage than as a single MFC, and capacitance increased with the length of time in parallel.

     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

湖北省生态足迹和生态承载力时空动态研究

研究湖北省内部可持续发展的趋势,以2005年、2010年、2013年统计数据为基础,应用生态足迹分析法核算湖北省17个市(州、林区)在三个时间节点的生态足迹与生态承载力,对各地区生态足迹和生态盈亏状况从时间和空间上进行动态研究。研究结果表明:湖北省17个市(州、林区)的总生态足迹和人均生态足迹呈迅速增长趋势,其中中部地区增长最快;2005年生态赤字最为严重的地区集中于以武汉为代表的中东部,2013年生态赤字最为严重的地区集中于中西部,生态赤字空间分布的重心逐渐由东部向西部转移;6种生态生产性土地增长速度由快到慢依次是建筑用地、化石能源、草地、水域、林地和耕地,其中潜江、恩施、天门、仙桃、黄冈等地建筑用地生态足迹增速最快,十堰、鄂州、黄石、荆州、荆门等地则是水域生态足迹增速最快,草地、水域、化石能源用地和耕地的生态足迹增长共同决定各地区人均生态足迹的变化趋势。     

The key elements analysis of Guangdong & Shenzhen ETS & tips for China national ETS construction

The only joint effort area of provincial and municipal governments resides in Guangdong Province and Shenzhen City in China’s carbon emission trading system (ETS) pilots, which characterize the national carbon ETS plots. The present study on the operating experience from this area has important reference value for the national carbon ETS. Analysis and comparison of the key elements show many differences in coverage, total allowance, allowance allocation, and MRV mechanism between Guangdong and Shenzhen carbon ETS. The present study provides the following explanation: (1) the design characteristics of carbon ETS (e.g. coverage, total quotas, the allocation, and MRV mechanism) depend on the local geographical conditions and policy goals. The differences of economic structure in Guangdong Province and Shenzhen City result in different coverage, which then result in differences in other management elements. (2) The operating state of the carbon market is affected by overall design of carbon ETS: in the case of tighter total allowance, lower proportion of China Certified Emission Reductions, and harsher punishment, the carbon market is relatively active, which intends to produce carbon financial market. Based on deep analysis of operation characteristics of carbon ETS in Guangdong and Shenzhen, the present study suggests that (1) the allowance should be allocated freely at the beginning stage and then gradually transited to the voluntary paid auction; (2) the allowances assigned to companies shall be linked up with their energy-saving objectives; (3) the output fluctuations and economic influence on the allowance allocation should be properly handled to maintain the fairness and consistence of allowance allocation standards; (4) stable public expectation is one of the key elements to maintain the regular operation of carbon ETS; (5) constrained carbon emission behavior outside ETS can contribute to social justice; and (6) the improvement of professional skills of relevant personnel in the enterprise and independent third party can enhance carbon emissions data reliability.