科尔沁沙地生态环境质量综合评价

基于研究区2000年土地利用图形数据、土地沙漠化监测数据、环境状况公报数据以及统计年鉴数据,采用中华人民共和国环境保护行业标准《生态环境状况评价技术规范（试行）》HJ／T192—2006）规定的生态环境状况评价的指标体系和计算方法。从旗、县尺度上对科尔沁沙地的生态环境状况进行综合评价。结果表明：2000年科尔沁沙地各旗县的生物丰度指数为22．01-37．96。植被覆盖指数为25．20～45．86,水网密度指数为22．51—30．97。土地退化指数为1．47—21．06。环境质量指数为57．98～59．84,综合生态环境质量指数为25．28～33．25。根据国家生态环境状况分级标准判断．该区域生态环境状况属于较差级别．意昧着该区域植被疆盖度较差。严重干旱少雨．物种较少。存在着明显限制人类生存的因素。     

基于行业增长的省级可再生能源规划目标分解

为了有效的把国家可再生能源规划分阶段的落实到各个省市,做好二者之间衔接工作,研究考虑了我国在从发展中国家过渡到中等发达国家时所需要考虑的如何平衡公平与效率之阀关系的问题,结合中国可再生能源发展实际特征,提出一种基于行业增长曲线的可再生能源阶段目标分解模型.模型首先求解出中国不同的可再生能源行业的增长曲线.然后给出可再生能源发展阶段目标函数.结合区域发展规划,给出了各省的阶段目标,为了验证该模型的正确性,以风电、水电和太阳发电为代表能源,结合国家可再生能源整体规划和资源需求分布状况,分阶段提出了各省域的可再生能源发展目标.目标分解参照两个原则(1)可分解原则.国家总量目标可以根据一定的标准在不同地区、部门和行业进行分解,由全社会共同完成发展目标.(2)公平性和区域差异的原则.考虑资源禀赋和社会经济发展水平差距,各地总量目标有所区别.研究对于可再生能源规划的整体实施和监管具有一定的借鉴意义.     

河床演变影响下桥区水域通航安全研究

为了科学地设定滩槽易变河段处的桥梁跨径布置方案,针对荆州长江大桥桥区水域河床演变影响通航安全的问题,采用实例调研及历史资料分析的方法,指出滩槽易变河段选址建设的桥梁,其最大风险是滩槽移位导致船舶从通航孔位置移动到非通航孔位置,提出新建桥梁通航孔布置应覆盖深泓和可能成为航道的深槽的摆动范围;根据提出的原则研究拟建的李埠桥...     

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.     

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.