鲢鱼放养控制北京城市河湖水华试验研究

介绍了利用鲢鱼控制北京城市河湖水华的试验研究,通过不同放养密度(0、17、51和103 g/m3)的现场围隔试验,对鲢鱼放养对水体水质和浮游生物的影响进行了分析.研究结果表明,鲢鱼放养使有鱼围隔中浮游生物量极低,从而使浮游植物基本不受浮游动物影响,而直接受鱼类影响;同时,中等密度(51 g/m3)的鲢鱼放养使水华蓝藻和微小藻的生长均得到了有效抑制,使藻类生物量最低.     

大气中痕量挥发性有机物的结构表征和保留时间的估计与预测

采用分子电性距离矢量（MEDV）表征大气中痕量挥发性有机物的分子结构,同时采用逐步回归结合统计检测对模型进行变量筛选,建立了大气中痕量挥发性有机物定量结构-色谱保留（QSRR）关系的8个变量和5个变量模型,两种QSRR模型的建模计算值复相关系数（R）分别为0.937和0.931;留一法（leave-one-out）交互校验复相关系数（RCV）分别为0.901和0.906,表明模型具有良好的估计能力与稳定性.     

敏捷网络集成方法在区域应急预案编制中的应用

为解决区域性突发事件应急管理预案的一致性及时效性问题,提出了区域应急组织分层网络集成重构的分级动态预案编制管理方法.结合区域性生物恐怖袭击事件应急管理案例分析,试图提高应急预案编制的实际可操作性.     

基于循环经济的煤矿环境评价指标体系

基于循环经济理论和煤矿开采中环境系统的演化规律.从资源循环与资源损失正反两个方面构建了煤矿区环境评估指标体系.指标体系分为目标层、指标类别层和单项指标层3个层次,单项指标层是具体舒化指标.由它直接计算指标类别层的值,进而计算目标层的评估结果.将此指标体系运用到重庆市某县煤矿,对该县近年来煤矿资源开发中环境可持续能力进行了计算和分析,评价结果与实际情况相符,揭示了该县煤矿开发中环境可持续能力提高的关键因素,进一步探讨了煤矿开发中环境能力提高和循环经济理念的实施建议.     

Spatial distribution of aerosol pollution based on MODIS data over Beijing, China

With the help of regression analysis,the relationships were detected between aerosol's contribution to apparent reflectance(ACR) derived from Moderate Resolution Imaging Spectroradiometer(MODIS)on board Terra and hourly PM_(10)mass concentration measured at 30 ground-based locations in Beijing for the August of 2003 and 2004.It was shown that there was a good correlation between the ACR and PM_(10)(linear correlation coefficient,R=0.56).On the basis of this relationship,spatial distribution and possible sources of PM_(10)derived from MODIS were analyzed and two frequently heavily-polluted regions were found,namely downtown of the city and the district near Xishan Mountain.These two regions coincidently are also urban heat island centers.The foundings of this paper will be greatly useful for environmental monitoring and urban planning for Beijing,especially for the 2008 Olympic game to be held in Beijing.     

Research and application of method of oxygen isotope of inorganic phosphate in Beijing agricultural soils

Phosphorus (P) in agricultural ecosystems is an essential and limited element for plants and microorganisms. However, environmental problems caused by P accumulation as well as by P loss have become more and more serious. Oxygen isotopes of phosphate can trace the sources, migration, and transformation of P in agricultural soils. In order to use the isotopes of phosphate oxygen, appropriate extraction and purification methods for inorganic phosphate from soils are necessary. Here, we combined two different methods to analyze the oxygen isotopic composition of inorganic phosphate (δ¹⁸O_P) from chemical fertilizers and different fractions (Milli-Q water, 0.5 mol L^?1 NaHCO₃ (pH = 8.5), 0.1 mol L^?1 NaOH and 1 mol L^?1 HCl) of agricultural soils from the Beijing area. The δ¹⁸O_P results of the water extracts and NaHCO₃ extracts in most samples were close to the calculated equilibrium value. These phenomena can be explained by rapid P cycling in soils and the influence of chemical fertilizers. The δ¹⁸O_P value of the water extracts and NaHCO₃ extracts in some soil samples below the equilibrium value may be caused by the hydrolysis of organic P fractions mediated by extracellular enzymes. The δ¹⁸O_P values of the NaOH extracts were above the calculated equilibrium value reflecting the balance state between microbial uptake of phosphate and the release of intracellular phosphate back to the soil. The HCl extracts with the lowest δ¹⁸O_P values and highest phosphate concentrations indicated that the HCl fraction was affected by microbial activity. Hence, these δ¹⁸O_p values likely reflected the oxygen isotopic values of the parent materials. The results suggested that phosphate oxygen isotope analyses could be an effective tool in order to trace phosphate sources, transformation processes, and its utilization by microorganisms in agricultural soils.     

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.