Molecular sex identification of sable and stone marten

Sable (Martes zibellina) and stone marten (Martes foina) populations are declining sharply and both species are threatened in China. Knowing the sex ratio of a population is important for long-term conservation and development of management plans for endangered marten species. We here describe the application of a novel PCR-RFLP method for sex determination in these two marten species. We developed a new designed primer pair (ZFLB1 and ZFLB2) that yields a smaller ZFX/ZFY PCR product (246 bp), which in combination with RFLP analysis, provides a simple and reliable method for gender determination in Martes zibellina and Martes foina. The method can be used on tissue and non-invasively collected hair samples for sex identification in wild marten populations, and is powerful for monitoring population status and trend of endangered martens.     

Urban Energy Efficiency from Cities in China and Policy Implications

Using 1995-2006 panel data for 210 cities, this article firstly measures total factor energy efficiency for Chinese cities with constant return to scale data envelopment analysis model,and then describes the features of regional differences. The results show that the changing trend displays four "reversed U" shapes. A turning point of widening gap occurred among cities in 2000 and there was a certain convergence among four regions. Urban energy efficiency level is unstable. Cities with low efficiency and high energy input will be the focus of energy-saving and reducing emissions. According to Tobit model, resource endowment, temperature, industrial structure, technology level and variety of policy factors should be treated differently in different situations.     

水环境中的有机磷阻燃剂及其生物富集和生物转化研究进展

随着世界范围内对溴代阻燃剂的禁用,有机磷阻燃剂(OPFRs)被作为替代品广泛应用于建筑、纺织、化工、电子以及家装材料等行业,生产和使用量逐年上升.由于OPFRs在环境和生物体中普遍检出,且多种化合物具有致癌性、神经毒性和生殖毒性,对生态环境和人体健康造成潜在威胁,目前已成为国际上的一个研究热点.笔者重点综述了河流、湖泊...     

Trends and variations of pH and hardness in a typical semi-arid river in a monsoon climate region during 1985–2009

The rapid growth of urbanization and industrialization, along with dramatic climate change, has strongly influenced hydrochemical characteristics in recent decades in China and thus could cause the variation of pH and general total hardness of a river. To explore such variations and their potential influencing factors in a river of the monsoon climate region, we analyzed a long-term monitoring dataset of pH, SO₄ ^2?, NO_x, general total hardness (GH), Mg²⁺, Ca²⁺, and Cl^? in surface water and groundwater in the Luan River basin from 1985 to 2009. The nonparametric Seasonal Kendall trend test was used to test the long-term trends of pH and GH. Relationship between the affecting factors, pH and GH were discussed. Results showed that pH showed a decreasing trend and that GH had an increasing trend in the long-term. Seasonal variation of pH and GH was mainly due to the typical monsoon climate. Results of correlation analysis showed that the unit area usage amounts of chemical fertilizer, NO₃ ^?, and SO₄ ^2? were negatively correlated with pH in groundwater. In addition, mining activity affected GH spatial variation. Acid deposition, drought, and increasing the use of chemical fertilizers would contribute to the acidification trend, and mining activities would affect the spatial variation of GH. Variations of precipitation and runoff in semi-arid monsoon climate areas had significant influences on the pH and GH. Our findings implied that human activities played a critical role in river acidification in the semi-arid monsoon climate region of northern China.     

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.     

The fractionation and geochemical characteristics of rare earth elements measured in ambient size-resolved PM in an integrated iron and steelmaking industry zone

Environmental Science and Pollution Research - Improved understanding of the fractionation and geochemical characteristic of rare earth elements (REEs) from steel plant emissions is important due...