基于智能能源网的资源开发与市场匹配模式研究——以煤基低碳能源转化为例

智能能源网是我国＂十二五＂期间完善资源开发及能源转化的重点科研攻关课题。以煤基低碳能源转化为例,提出基于智能能源网的资源开发模式。该模式可从根本上改善我国传统的对原生资源低效和掠夺式的开发方式,提高从原生资源到能源商品整个过程的资源转化效率,在极大程度上减少生产过程中对环境造成的碳排放污染。其市场匹配模式应以政府作为引导主体,按市场化所处的不同阶段积极施行经济和科技体制改革。     

道路交通噪声预测模式预测结果的比较

为了分析实际环境影响评价中常用的各种公路交通噪声预测模型预测结果之间存在的差异,并验证各预测方式与实测值之间的相符性,通过对选取的高速公路和市政快速公路采用各种预测模型计算比较,并用实际监测值对各模型预测结果进行验证,结果发现,不同的预测方式会造成预测结果之间昼间4～9 dB、夜间5～10 dB的差异,采用2006版规范计算车速和单车噪声源强,距离衰减考虑车流量大小的预测方式得到的预测结果与实测值最为接近。     

Mathematical Modeling of Leachates from Ash Ponds of Thermal Power Plants

The present study describes the development of empirical models for the prediction of various trace metals i.e., Mn, Cu, Fe, Zn and Pb found in the leachates generated from the ash ponds of various thermal power plants. The dispersion phenomenon of these trace metals followed first order reaction rate kinetics. The empirical models for individual trace metals derived from the lab scale models data correlate well with the real field data with regression coefficients varying from 0.93 to 0.98. The predicted concentrations of the trace metals varied within ±3% of the observed values in the leachates generated from the ash ponds of four thermal power plants with standard deviation varying from 0.001 to 0.032. The empirical models derived from the study can be applied for prediction of trace metals in leachates generated from similar thermal power plants.     

Long-range transport of acidifying substances in East Asia—Part II: Source–receptor relationships

Region-to-grid source–receptor (S/R) relationships are established for sulfur and reactive nitrogen deposition in East Asia, using the Eulerian-type Community Multiscale Air Quality (CMAQ) model with emission and meteorology data for 2001. We proposed a source region attribution methodology by analyzing the non-linear responses of the CMAQ model to emission changes. Sensitivity simulations were conducted where emissions of SO₂, NO_x, and primary particles from a source region were reduced by 25%. The difference between the base and sensitivity simulations was multiplied by a factor of four, and then defined as the contribution from that source region. The transboundary influence exhibits strong seasonal variation and generally peaks during the dry seasons. Long-range transport from eastern China contributes a significant percentage (>20%) of anthropogenic reactive nitrogen as well as sulfur deposition in East Asia. At the same time, northwestern China receives approximately 35% of its sulfur load and 45% of its nitrogen load from foreign emissions. Sulfur emissions from Miyakejima and other volcanoes contribute approximately 50% of the sulfur load in Japan in 2001. Sulfur inflows from regions outside the study domain, which is attributed by using boundary conditions derived from the MOZART global atmospheric chemistry model, are pronounced (10–40%) over most parts of Asia. Compared with previous studies using simple Lagrangian models, our results indicate higher influence from long-range transport. The estimated S/R relationships are believed to be more realistic since they include global influence as well as internal interactions among different parts of China.     

Long-term trends and short-term variability of water quality in Skive Fjord, Denmark – nutrient load and mussels are the primary pressures and drivers that influence water quality

Nineteen years of monitoring data from the eutrophic Skive Fjord, Denmark were examined for linkages to external pressures and drivers, including nutrient inputs, meteorology and stocks of blue mussels. Linkages were examined by: 1) time-series analysis to document effects of nutrient reduction programs, 2) Pearson Rank correlations, 3) multivariate statistical analysis (PLS) to identify water quality variables with high predictability and their linkages to pressures, and 4) regression analysis to quantify relationships between pressures and water quality. Freshwater input, nitrogen load and phosphorus load showed decreasing trends through the period 1984–2002. The load reductions were only partially translated into trends in water quality: phosphorus decreased in most seasons, while total nitrogen decreased during winter and spring only. Phosphorus concentration had the highest predictability (explained by seasonal temperature variation) followed by transparency, silicate, tot-N, chlorophyll-a, primary productivity, phytoplankton diversity and phytoplankton turnover. The variation in pressures other than nutrient input confounded the relations between loads and water quality. High biomass of mussels led to reduced chlorophyll-a and increased transparency, while short-term variability in water column mixing led to changes in chlorophyll-a due to nutrient entrainment and coupling to benthic mussels.     

An overview of systems analysis methods in delineating environmental quality indices

Environmental quality indices (EQIs) have been developed for a variety of purposes ranging from enforcement of environmental standards, to analysis of trends of environmental degradation or improvement, to scientific research. EQIs currently in use are not organized within an integrated framework and thus it has been difficult to analyze adequately complex, multidisciplinary, large-scale, global phenomena. In this paper we compare four different approaches to developing EQIs within a systems perspective. Our analysis suggests that: (1) non-linear regression models that represent an ecosystem's response to different impacts within a stress-response framework (method of response functions) are useful tools for analysis of environmental data; (2) non-equilibrium thermodynamics models based on the concept of exergy, which represents the free energy a system possesses in relation to its environment, provide a common basis for representing many aspects of ecosystem development and response to environmental impacts as a single measure; (3) diagram models based on the concept of emergy, which represents both environmental values and economic values with a single measure, provide a common basis for integrating economic development and environmental protection values into one index; and (4) complex systems simulation models based on general systems theory, which use the methodologies of systems analysis and simulation to identify, quantify, and interrelate EQIs within a dynamic systems context, provide explicit linkages between causes and effects (vertical integration) and identify cross-linkages among different environmental issues (horizontal integration).     

Modeling and risk assessment for soil temperatures beneath prescribed forest fires

Prescribed fire is a management tool used by wildland resource management organizations in many ecosystems to reduce hazardous fuels and to achieve a host of other objectives. To study the effects of fire in naturally accumulating fuel conditions, the ambient soil temperature is monitored beneath prescribed burns. In this study we developed a stochastic model for temperature profiles (values at 15 minute intervals) recorded at four depths beneath the soil during a large prescribed burn study. The model was used to assess the temporal fit of the data to particular solutions of the heat equation. We used a random effects model to assess the effects of observed site characteristics on maximum temperatures and to estimate risks of temperatures exceeding critical levels in future similar prescribed fires. Contour plots of estimated risks of temperatures exceeding 60°C for a range of fuel levels and soil depths indicated high risks of occurrence, especially when the moisture levels are low. However, the natural variability among sites seems to be large, even after controlling fuel and moisture levels, resulting in large standard errors of predicted risks.     

多氯联苯光化学降解研究的最新进展

综述了近10年以来多氯联苯(PCBs)在有机相、表面活性剂中的直接光化学降解行为,以及PCBs在水相中的光敏化反应和光催化反应等间接光化学降解行为。从光解动力学、光解途径、光解机理以及光解产物等几方面阐述了PCBs的光化学行为,光催化降解有机污染物是一种很有发展前景的有机污染物治理技术。     

Short-term wind speed forecasting: Application of linear and non-linear time series models

This study forecasts day-ahead wind speed at 15 minute intervals at the site of a wind turbine located in Maharashtra, India. Wind speed exhibits non-stationarity, seasonality and time-varying volatility clustering. Univariate linear and non-linear time series techniques namely MSARIMA, MSARIMA-GARCH and MSARIMA-EGARCH have been employed for forecasting wind speed using data span ranging from 3 days to 15 days. Study suggests that mean absolute percentage error (MAPE) values first decrease with the increase in data span, reaches its minima and then start increasing. All models provide superior forecasting performances with 5 days data span. It is further evident that ARIMA-GARCH model generates lowest MAPE with 5 days data span. All these models provide superior forecasts with respect to current industry practices. This study establishes that employing various linear and non-linear time series techniques for forecasting day-ahead wind speed can benefit the industry in terms of better operational management of wind turbines and better integration of wind energy into the power system, which have huge financial implications for wind power generators in India.