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.     

Impediments to River Rehabilitation

Rehabilitation of river ecosystems is generally initiated when a river is no longer able to provide benefits to humans and other living beings. The major purposes of river rehabilitation projects are improving water quality, conserving nature, rehabilitating fisheries and riparian habitats, flood mitigation, and creating recreational opportunities. For a river rehabilitation project to be successful, it must have specific and clearly explained objectives, although these objectives will vary for each project depending upon factors such as economics, public demand, ecosystem benefits, and the like. In this article, we provide a critical analysis of the purposes that drive river rehabilitation projects and we identify and discuss impediments that can cause a project to deviate from its intended goals. We also discuss major constraints on rehabilitation efforts that can result in failed projects.     

基于MODIS地面温度数据的成都市热岛时空变化

研究利用2000、2005和2010年逐月的MODIS/Terra和MODIS/Aqua卫星的地表温度数据产品,通过等间距法进行分析,得出成都市建成区2000~2010年间昼夜热岛的时空变化以及相互间差异。研究结果表明:成都市昼夜热岛存在显著差异,日间热岛主要呈散点分布,夜间热岛集中在城市中心区域。日夜间热岛变化趋势有显著不同,日间热岛继续分散而夜间热岛范围则有所扩大。日间热岛分布与植被覆盖存在一定的负相关性,但道路也在一定程度上促使了地面温度的增加,夜间城市热岛范围及变化则与其下垫面组成存在关系。     

Pollination ecology of <Emphasis Type="Italic">Eomecon chionantha</Emphasis> Hance (Papaveraceae), an endemic species in China

Eomecon chionantha Hance, the only species in the genus Eomecon, is an angiosperm species endemic to China. Here we investigated the pollination syndrome, pollinator generalization of E. chionantha. Our results showed that the individual flower longevity of E. chionantha was 3–5 days. The flowering phenology of E. chionantha can be divided into five phases. We found significant differences in the pollination efficiency of the different insects visiting E. chionantha with bees and flies being the most efficient pollinators. This, in addition to floral morphology and phenology, suggests that E. chionantha is generalized on bees and flies and possesses an outcrossing, partially self-compatible and pollinator-dependent breeding system.     

Climate variability and changes in the major cities of Bangladesh: observations,possible impacts and adaptation

High population density, inadequate infrastructure and low adaptive capacity have made the urban population of Bangladesh highly vulnerable to climate change. Trends in climate and climate-related extreme events in five major cities have been analyzed in this paper to decipher the variability and ongoing changes in urban Bangladesh. An analysis of 55 years (1958–2012) of daily rainfall and temperature data using nonparametric statistical methods shows a significant increase in annual and seasonal mean daily maximum and minimum temperatures in all five cities. A significant increase in climate-related extreme events, such as heavy rainfall events (>20 mm), hot days (>32 °C) and hot nights (>25 °C), is also observed. Climate model results suggest that these trends will continue through the twenty-first century. Vulnerability of urban livelihoods and physical structures to climate change is estimated by considering certainty and timing of impacts. It has been predicted that public health and urban infrastructures, viz. water and power supply, would be the imminent affected sectors in the urban areas of Bangladesh. Adaptation measures that can be adopted to mitigate the negative impacts of climate change are also discussed.     

中国工业水污染及其成因的de Bruyn模型分析

选取废水、挥发酚、氰化物、COD、石油类和氨氮为中国工业水污染指标,利用分解分析方法将2004—2010年间的污染变化分解为规模效应、结构效应、污染治理效应、清洁技术效应和广义技术效应。结果显示,这5类效应的平均作用强度分别为2.08%、3.04%、15.61%、17.37%和32.88%,其中规模效应和广义技术效应是影响工业水污染的主导效应。各类效应对不同污染物的作用方向并不完全一致,规模效应促进污染物排放量的增加;结构效应以加重污染为主,污染治理效应和清洁技术效应以减轻污染为主;广义技术效应的平均作用强度和负向作用概率均最大,是现阶段中国工业水污染控制最为有效的手段。     

MFC强化同步短程硝化反硝化工艺的启动

微生物燃料电池(MFC)可在阴极实现反硝化、短程反硝化和同步硝化反硝化并产生电能,但在MFC阴极实现同步短程硝化反硝化的研究尚未见到报道。为了探讨MFC阴极同步短程硝化反硝化工艺的性能,将双室曝气阴极MFC与A/O脱氮工艺结合处理人工模拟低碳氮比废水。通过静置运行15 d使得MFC阴极室亚硝态氮得以积累,氨氧化菌得以富集。随即改为连续运行后第21天成功启动同步短程硝化反硝化MFC;阴极出水氨氮浓度为0.3 mg/L,亚硝态氮浓度为15.9 mg/L,硝态氮浓度为0.6 mg/L,亚硝化率达到95%以上,阴极电极自养反硝化去除率达到50%以上,COD去除率达到85%以上。结果表明,将MFC与同步短程硝化反硝化工艺结合,通过阴极室中氧气得电子获得高p H,可以强化同步短程硝化反硝化工艺,完成生物脱氮的同时回收电能,并具有减少外加碱度的优势。