New Development in Study of the Faustmann Optimal Forest Harvesting

Chinese forest resources have become very scarce in the face of rapid economic growth demand, while the reform of collective forest right system is in full swing across the country. It will directly affect the regeneration level of forest resources and the diverse ecological value functions. In this article, the mainstream model paradigm of forest economics, that is, the basic framework of the Faustmann model and its evolution process are made in a more detailed explanation, especially the extended model in...     

Synthesis,Characterization and Nanocomposite Formation of Poly(glycerol succinate-co-maleate) with Nanocrystalline Cellulose

A novel biodegradable polymer based on glycerol, succinic anhydride and maleic anhydride, poly(glycerol succinate-co-maleate), poly(GlySAMA), was synthesized by melt polycondensation and tested as a matrix for composites with nanocrystalline cellulose. This glycerol-based polymer is thermally stable as a consequence of its targeted cross-linked structure. To broaden its range of properties, it was specifically formulated with nanocrystalline cellulose (NCC) at concentrations of 1, 2 and 4 wt%, and showed improved mechanical properties with NCC. Specifically, the effect of reinforcement on mechanical properties, thermal stability, structure, and biodegradability was evaluated, respectively, by tensile tests and thermogravimetric analyses, X-ray diffraction and respirometry. The neat poly(GlySAMA) polymer proved flexible, exhibiting an elongation-to-break of 8.8 % while the addition of nanowhiskers (at 4 wt%) caused tensile strength and Young’s modulus to increase, 20 and 40 %, respectively. Stiffness improved without significantly decreasing thermal stability as measured by thermogravimetric analysis. Biodegradation tests indicated that all samples were degradable but NCC reduced the rate of biodegradation.     

Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island

Surveillance for invasive non-indigenous species (NIS) is an integral part of a quarantine system. Estimating the efficiency of a surveillance strategy relies on many uncertain parameters estimated by experts, such as the efficiency of its components in face of the specific NIS, the ability of the NIS to inhabit different environments, and so on. Due to the importance of detecting an invasive NIS within a critical period of time, it is crucial that these uncertainties be accounted for in the design of the surveillance system. We formulate a detection model that takes into account, in addition to structured sampling for incursive NIS, incidental detection by untrained workers. We use info-gap theory for satisficing (not minimizing) the probability of detection, while at the same time maximizing the robustness to uncertainty. We demonstrate the trade-off between robustness to uncertainty, and an increase in the required probability of detection. An empirical example based on the detection of Pheidole megacephala on Barrow Island demonstrates the use of info-gap analysis to select a surveillance strategy.     

Arctic Lake Physical Processes and Regimes with Implications for Winter Water Availability and Management in the National Petroleum Reserve Alaska

Lakes are dominant landforms in the National Petroleum Reserve Alaska (NPRA) as well as important social and ecological resources. Of recent importance is the management of these freshwater ecosystems because lakes deeper than maximum ice thickness provide an important and often sole source of liquid water for aquatic biota, villages, and industry during winter. To better understand seasonal and annual hydrodynamics in the context of lake morphometry, we analyzed lakes in two adjacent areas where winter water use is expected to increase in the near future because of industrial expansion. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus imagery acquired between 1985 and 2007 were analyzed and compared with climate data to understand interannual variability. Measured changes in lake area extent varied by 0.6% and were significantly correlated to total precipitation in the preceding 12 months (p < 0.05). Using this relation, the modeled lake area extent from 1985 to 2007 showed no long-term trends. In addition, high-resolution aerial photography, bathymetric surveys, water-level monitoring, and lake-ice thickness measurements and growth models were used to better understand seasonal hydrodynamics, surface area-to-volume relations, winter water availability, and more permanent changes related to geomorphic change. Together, these results describe how lakes vary seasonally and annually in two critical areas of the NPRA and provide simple models to help better predict variation in lake-water supply. Our findings suggest that both overestimation and underestimation of actual available winter water volume may occur regularly, and this understanding may help better inform management strategies as future resource use expands in the NPRA.     

A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources

Abstract

Neural networks have shown tremendous promise in modeling complex problems. This work describes the development and validation of a neural network for the purpose of estimating point source emission rates of hazardous gases. This neural network approach has been developed and tested using experimental data obtained for two specific air pollutants of concern in West Texas, hydrogen sulfide and ammonia. The prediction of the network is within 20% of the measured emission rates for these two gases at distances of less than 50 m. The emission rate estimations for ground level releases were derived as a function of seven variables: downwind distance, crosswind distance, wind speed, downwind concentration, atmospheric stability, ambient temperature, and relative humidity. A backpropagation algorithm was used to develop the neural network and is also discussed here. The experimental data were collected at the Wind Engineering Research Field Site located at Texas Tech University in Lubbock, Texas. Based on the results of this study, the use of neural networks provides an attractive and highly effective tool to model atmospheric dispersion, in which a large number of variables interact in a nonlinear manner.     

Preparation of activated carbons from raw and biotreated agricultural residues for removal of volatile organic compounds

Activated carbons with diverse physical and chemical properties were produced from four agriculture residues, including raw barley husk, biotreated barley husk, rice husk, and pistachio shell. Results showed that with adequate steam activation (30-90 min, 50% H2O(g),/50% N2), activated carbons with surface areas between 360 and 950 m2 g(-1) were developed. Further increases in the activation time destroyed the pore structure of activated carbons, which resulted in a decrease in the surface area and pore volume. Biotreated agricultural residues were found to be suitable precursors for producing mesoporous activated carbons. The oxygen content of activated carbons increased with increasing activation time. Results from X-ray photoelectron spectroscopy examination further suggested that H2O molecules react with the carbon surface, enhancing the deconvoluted peak area of carbonyl and carboxyl groups. Equilibrium adsorption of toluene indicated that the adsorption capacities increased with an increase in the inlet toluene concentration and a decrease in temperature. The adsorption isotherms were successfully fitted with Freundlich, Langmuir, and Dubinin-Radushkevich equations. Activated carbons derived from agricultural residues appear to be more applicable to adsorb volatile organic compounds at a low concentration and high-temperature environment.