关于环境保护的可持续发展

阐述了可持续性发展的定义和内涵，介绍了可持续性发展概念产生的历史背景和该概念不断完善和补充的过程。并论述了经济增长、环境保护与可持续性发展的关系，最后，提出了实施可持续发展战略应注意的一些问题。     

清洁安全的井下作业环境

根据煤矿井下粉尘的特性及其危害和对井下空气污染的状况，提出针对井下各产尘源的治理措施和治理方法，通过对粉尘污染的治理，改善煤矿工人的作业环境，实现清洁生产，降低矿工职业病发病率和事故率，提高煤矿生产效益和经济效益。     

城市汽车空气污染控制基准研究

本文论述了城市汽车空气污染研究的目标、基本方法及城市汽车污染控制区的划分原则和方法.汽车类型流动源采用整个控制区平均浓度作为分析对比的基础不合适,应该专门研究确定基准控制点.为了合理选择确定控制计算点,提出3种方法:峰值法、核心区和重点控制区法、9点平均法.9点平均法可以有效增加各类污染源的敏感点,同时又可避免增加该点位影响过大的缺陷.这种方法在其他类型大气污染源控制中也能发挥作用.     

黑河流域生态环境的脆弱性及其对地下水的依赖性

本文通过翔实的素材阐述了河西走廊黑河流域生态环境的脆弱性及其对地下水环境的依赖性,根据野外实地考察实料揭月了适合西北生态脆弱区的地下水可持续利用模式,并指出了生态环境保护与水资源利用协调发展的国土安全意义.     

黄河断流与黄河的水资源承载力

分析了黄河断流的时空特征、根源和危害，提出了水资源承载力的概念。黄河的水资源承载力即指在生态系统良性循环的条件下，黄河对人类取用水资源的最大承受能力。只有建立起水资源承载力的概念，人类才会谨慎地取用水资源，小心地保护水资源。     

陶庄煤矿矿震灾害与防治

本文通过对陶庄煤矿矿区地质构造及矿震分布特征的分析，探讨了矿震的发生条件及其机理，提出了矿震与天然地震的关联性，阐明了矿震的前兆特征及防治措施。     

灾害对国民经济影响的定量分析模型及其应用

自然灾害对国民经济和人民生活能造成巨大的损失 ,因此 ,研究灾害对国民经济的影响具有重要的意义。灾害系统是一个十分复杂的系统 ,直接建立灾害对国民经济影响的定量分析模型比较困难。本文基于投入产出方法 ,讨论了直接和间接经济损失在投入 -产出表中的表达方式 ,建立了灾害损失评估的定量分析模型 ,并以农业为例 ,分析了自然灾害造成的农业总产值损失对整个经济系统的影响。     

鄱阳湖洪灾特征与圩区还湖减灾运用方式研究

根据水文资料,分析了鄱阳湖入湖五大河流来水与长江水情对鄱阳湖洪灾的影响和鄱阳湖洪水位的频率特征,探讨了湖区中小圩区"高水还湖滞洪,低水种植养殖"的减灾运行方式对湖区防洪减灾可以起到的作用。     

WATER QUALITY MODELING OF ALTERNATIVE AGRICULTURAL SCENARIOS IN THE U.S. CORN BELT1

ABSTRACT: Simulated water quality resulting from three alternative future land‐use scenarios for two agricultural watersheds in central Iowa was compared to water quality under current and historic land use/land cover to explore both the potential water quality impact of perpetuating current trends and potential benefits of major changes in agricultural practices in the U.S. Corn Belt. The Soil Water Assessment Tool (SWAT) was applied to evaluate the effect of management practices on surface water discharge and annual loads of sediment and nitrate in these watersheds. The agricultural practices comprising Scenario 1, which assumes perpetuation of current trends (conversion to conservation tillage, increase in farm size and land in production, use of currently‐employed Best Management Practices (BMPs)) result in simulated increased export of nitrate and decreased export of sediment relative to the present. However, simulations indicate that the substantial changes in agricultural practices envisioned in Scenarios 2 and 3 (conversion to conservation tillage, strip intercropping, rotational grazing, conservation set‐asides and greatly extended use of best management practices (BMPs) such as riparian buffers, engineered wetlands, grassed waterways, filter strips and field borders) could potentially reduce current loadings of sediment by 37 to 67 percent and nutrients by 54 to 75 percent. Results from the study indicate that major improvements in water quality in these agricultural watersheds could be achieved if such environmentally‐targeted agricultural practices were employed. Traditional approaches to water quality improvement through application of traditional BMPs will result in little or no change in nutrient export and minor decreases in sediment export from Corn Belt watersheds.     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.