基于非线性响应函数和蒙特卡洛模拟的滇池流域污染负荷削减情景分析

为定量了解滇池外海水质对流域污染负荷削减率的响应变化程度,并评估相关决策的潜在风险,在长期的水质模拟结果基础之上,本文使用内嵌非线性响应函数的蒙特卡罗模拟方法,在2个不确定性水平下（5%和10%的变异范围）分析滇池外海为达到3个水质目标情景（Ⅲ类、Ⅳ类和Ⅴ类）的污染负荷削减及其不确定性.模型结果表明,在2种不确定性水平...     

结合外场观测分析珠三角二次有机气溶胶的数值模拟

结合2008年11月18~25日期间珠三角地区的二次有机气溶胶(SOA)外场观测数据,验证区域空气质量模式WRF/Chem(weather research and forecasting model with chemistry)中两种SOA化学机制——VBS(volatile basis set)和SORGAM(secondary organic aerosol model)对珠三角SOA的模拟效果.VBS机制考虑了更为广泛的SOA前体物和化学老化过程,SOA模拟值更接近观测值,能合理反映SOA观测值的逐天变化趋势,与观测值的平均绝对偏差和相关性分别是-4.88μg·m-3和0.91,而SORGAM机制的分别为-5.32μg·m-3和0.18.利用VBS机制模拟区域内SOA的时空分布,结果显示SOA浓度具有显著的昼夜变化特征,浓度峰值出现在中午时段.受到输送和臭氧区域分布的影响,各城市SOA浓度差异显著,下风向的城市(如中山、珠海、江门)SOA浓度较高.     

A numerical study of summer ozone concentration over the Kanto area of Japan using the MM5/CMAQ model

We assessed the ability of the MM5/CMAQ model to predict ozone (O3) air quality over the Kanto area and to investigate the factors that a ect simulation of O3. We find that the coupled MM5/CMAQ model is a useful tool for the analysis of urban environmental problems. The simulation results were compared with observational data and were found to accurately replicate most of the important observed characteristics. The initial and boundary conditions were found to have a significant e ect on simulated O3 concentrations. The results show that on hot and dry days with high O3 concentration, the CMAQ model provides a poor simulation of O3 maxima when using initial and boundary conditions derived from the CMAQ default data. The simulation of peak O3 concentrations is improved with the JCAP initial and boundary conditions. On mild days, the default CMAQ initial and boundary conditions provide a more realistic simulation. Meteorological conditions also have a strong impact on the simulated distribution and accumulation of O3 concentrations in this area. Low O3 concentrations are simulated during mild weather conditions, and high concentrations are predicted during hot and dry weather. By investigating the e ects of di erent meteorological conditions on each model process, we find that advection and di usion di er the most between the two meteorological regimes. Thus, di erences in the winds that govern the transport of O3 and its precursors are likely the most important meteorological drivers of ozone concentration over the central Kanto area.     

湖泊暴雨径流水质模拟研究

根据暴雨径流污染物浓度变化特点，采用最小二乘法对暴雨期间污染源各监测数据进行回归分析处理，对湖底糙率采用自动调整处理，建立了湖泊暴雨径流水质模型．对滇池湖泊某次暴雨过程的总磷和总氮进行了模拟研究，计算结果表明，该模型应用于滇池湖泊是成功的．     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

干旱区绿洲系统生态-生产-生活承载力相互作用的驱动机制分析

绿洲系统承载力是生态承载力、生产承载力与生活承载力3种承载力相互耦合的产物。绿洲系统生态-生产-生活承载力相互作用的驱动机制主要通过水资源、投资和人口三大核心因子的驱动进行,实现水资源生态效益、经济效益与社会效益最大化的内在驱动,决定着绿洲系统生态-生产-生活承载力的最佳组合方式。在边际效益递减规律约束下,投资分配的最佳比例取决于单位投资效益的差异。人口通过改变核心驱动因子---水资源与投资的稀缺程度,在绿洲系统生态-生产-生活承载力相互作用驱动机制中起着双重作用。     

基于农户行为的土地利用人工社会模型的构造与应用

为探讨人工社会在土地利用变化研究领域中的应用,研究创建了一个模拟土地利用变化过程的人工社会模型(LUC-ASM)。该模型以农户和农民为两类主体(agents),模拟分析不同社会经济条件和政策影响下,两类主体对自己承包土地利用方式的微观决策所可能产生的宏观效应及其时空变化特征。以鄱阳湖区一个现实村落为主要研究对象,以农户从事各类生产活动的行为特征为准则,利用LUC-ASM模型模拟未来30年内该村落土地利用变化过程。结果表明,未来这一地区土地利用的变化与人口增长结构调整方式、国家土地利用与环保政策、社会经济发展态势等因素密切相关;在城市化的影响下,该地区的农业劳动力将会出现短缺,农村土地有可能被撂荒,需要及早采用政策措施予以避免。总体而言,LUC-ASM模型能够形象地反映土地利用变化过程的微观驱动机理及各种社会经济与环境因素影响的宏观效应,有助于深化我们对人与自然环境相互作用的认识。     

《土地利用现状分类》国家标准的解读

2007年8月,我国新的土地利用分类系统(《土地利用现状分类)》首次作为国家标准颁布,标志我国土地利用研究的深入和土地管理水平的提高。为促进其更好地实施应用,论文对该标准进行了全面解读。在介绍《土地利用现状分类》国家标准主要内容的基础上,将新的分类标准与原有的《土地分类(试行)》标准进行了比较分析,指出了新的分类在土地利用类型的归并、调整等方面的改进之处及存在的问题,提出了新旧分类系统过渡和衔接的建议,为下一步国家制订新标准的实施细则提供了依据。     

环境经济系统分类及协调发展判据研究

应用系统论对环境经济系统进行结构分析,并根据内部相互作用强弱对环境经济系统进行了分类;建立了环境经济协调发展判据,对区域环境经济协调发展问题进行了定量研究。     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (− 0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.