Engineering survey of the Environment and Disaster Monitoring and Forecasting Small Satellite Constellation

The Environment and Disaster Reduction Satellite Constellation is a small satellite constellation developed by China for disaster monitoring.The two primary optical satellites,HJ-A and HJ-B,were successfully launched in September 2008.The satellites carry a charge-coupled device,hyperspectral imager,and infrared scanner,and have the capability for wide coverage and rapid revisits in disaster reduction applications.Also scheduled to be launched is the HJ-C,which will carry synthetic aperture radar and have the ability to provide allweather observations at any time.A follow-up‘4
4’satellite constellation including four optical satellites and four radar satellites is in the works,to achieve the capability of quantitative,all-weather,all-time disaster forecasting,monitoring and assessment.The corresponding disaster reduction application system has a series of functions including remote sensing data processing,disaster monitoring and assessment,decision support,and user service and information distribution,which serves the whole process of disaster management.Since its construction has been carried out,the system has successfully dealt with several huge domestic and international natural disasters,and effectively improved scientific decision support.The follow-up system’s construction will integrate,update,and extend the original system to fulfill large-scale,quantitative,allweather disaster operation application needs.

Nanomaterials for environmental burden reduction,waste treatment,and nonpoint source pollution control:a review

Nanomaterials are applicable in the areas of reduction of environmental burden,reduction/treatment of industrial and agricultural wastes,and nonpoint source(NPS)pollution control.First,environmental burden reduction involves green process and engineering,emissions control,desulfurization/denitrification of nonrenewable energy sources,and improvement of agriculture and food systems.Second,reduction/treatment of industrial and agricultural wastes involves converting wastes into products,groundwater remediation,adsorption,delaying photocatalysis,and nanomembranes.Third,NPS pollution control involves controlling water pollution.Nanomaterials alter physical properties on a nanoscale due to their high specific surface area to volume ratio.They are used as catalysts,adsorbents,membranes,and additives to increase activity and capability due to their high specific surface areas and nano-sized effects.Thus,nanomaterials are more effective at treating environmental wastes because they reduce the amount of material needed.

Preparation of Monodispersed Copper Nanoparticles by an Environmentally Friendly Chemical Reduction

In this paper, highly dispersive nanosized copper particles with a mean particle size of less than 6 nm are prepared by an environmentally friendly chemical reduction method. Non-toxic L-ascorbic acid acts as both reducing agent and antioxidant in ethylene glycol in the absence of any other capping agent. Transmission electron microscopy （TEM） is used to characterize the size and morphology of Cu nanoparticles. The results of UV-Vis spectroscopy （UV-Vis）, energy dispersive spectroscopy （EDS） and high resolution TEM （HRTEM） illustrate that the resultant product is pure Cu nanocrystals. The size of Cu nanoparticles is remarkably impacted by the order of reagent addition, and the investigation reveals the reaction procedure of Cu^2＋ ions and L-ascorbic acid.

Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.