博斯腾湖水质矿化度模型及预测研究

根据博斯腾湖１９８５～１９９５年水质监测数据和出入湖水量等水文数据观测值，采用水质扩散模型和盐量平衡关系推导出博斯腾湖大湖区（简称博湖）水质矿化度模型，并预测了几种情况下博湖水质矿化度及近几年变化趋势，分析了影响博湖水质矿化度的主要因素，为博湖的近期和远期环境保护规划、环境管理等提供科学依据．     

泰州市环境空气中挥发性有机物分布特征

于2018年4—9月对泰州市环境空气中挥发性有机物(VOCs)组分开展现场观测,结合观测数据分析该市大气中VOCs的时空分布特征。结果表明:观测期间泰州市环境空气中VOCs平均摩尔比为45.1 nmol/mol,其中含氧挥发性有机物占比为57.8%;受周边排放源和地理位置影响,下风向点位的VOCs测定值高于其他点位;VOCs月均最高值出现在6月,与臭氧月均最高值出现时间一致,7—9月气团出现老化,导致臭氧生成能力减弱;观测期间VOCs中甲苯/苯(T/B)比值范围为0.201 9～5.130 3,且大部分T/B比值2,说明溶剂、油气和液化石油气挥发等排放源对泰州市环境空气中VOCs的影响较为显著。     

水环境数学模型在河流水质管理规划中的应用

本文讨论了水环境数学模型的选择及在水质管理规划中的使用方法,达到实用的目的.具有广泛的通用性和实用性.     

灰色系统模型GM（1,1）在大气环境浓度预测中的应用

运用灰色系统理论，结合实例详细介绍了ＧＭ（１，１）模型在大气环境浓度预测中的应用。     

浅谈GAM与GM（1,1）模型

对ＧＡＭ水环境预测模型提出了四点不同看法，与有关作者商榷，指出ＧＭ（１，１）模型与ＧＡＭ模型二者并无优劣之差，精度亦相当。     

关于模糊数学在环境质量综合评判中“最大隶属度”原则不适用性的讨论

通过对模糊数学在环境质量综合评判中的分析，指出“最大隶属度”原则的不适用性，并根据环境质量分级所存在的固有关系提出改进的方法。     

Euclidean贴近度水质评价法及其应用

分析了模糊综合评判在水质评价中的缺陷,将模糊数学中的贴近度与经典的Euclidean距离结合,提出Euclidean贴近度水质评价法。结果表明,该评价方法具有科学性、合理性,并且精度高,简单实用。     

灰色系统GM（1,1）模型在城市地下水总硬度预测中的应用

应用灰色系统GM(1,1)模型,对地下水中总硬度的变化作了预测.检验结果表明,该模型精度较高,是一种较好的预测方法.     

Emissions of gaseous and particulate pollutants in a port and harbour region in India

Ports can generate large quantity of pollutants in the atmosphere due to various activities like loading and unloading,transportation, and construction operations. Determination of the character and quantity of emissions from individual sources is an essential step in any project to control and minimize the emissions.In this study a detailed emission inventory of total suspendedparticulate matter (TSP), particulate matter less than 10 m(PM₁₀), sulfur dioxide (SO₂) and nitrogen oxides (NO_x) for a port and harbour project near Mumbai is compiled. Results show that the total annual average contributions of TSP and PM₁₀ from all the port activitieswere 872 and 221 t yr^-1, respectively. Annual average emissions of gaseous pollutants SO₂ and NO_xwere 56 and 397 t yr^-1, respectively, calculatedby using emission factors for different port activities. The maximum TSP emission (419 t yr ^-1) was from paved roads, while the least (0.4 t yr^-1) was from bulk handling activity. The maximum PM₁₀ emission (123 t yr^-1) was from unpaved roads and minimum (0.2 t yr^-1) from bulk handling operations. Similarly the ratio of TSP and PM₁₀ emission was highest (5.18) from paved roads and least (2.17) from bulk handling operations. Regression relation was derivedfrom existing emission data of TSP and PM₁₀ from variousport activities. Good correlation was observed between TSP andPM₁₀ having regression coefficient >0.8.     

Mega-Development Trends in the Amazon: Implications for Global Change

This paper describes four global-change phenomena that are having major impacts on Amazonian forests. The first is accelerating deforestation and logging. Despite recent government initiatives to slow forest loss, deforestation rates in Brazilian Amazonia have increased from 1.1 million ha yr^–1 in the early 1990s, to nearly 1.5 million ha yr^–1 from 1992–1994, and to more than 1.9 million ha yr^–1 from 1995–1998. Deforestation is also occurring rapidly in some other parts of the Amazon Basin, such as in Bolivia and Ecuador, while industrialized logging is increasing dramatically in the Guianas and central Amazonia.The second phenomenon is that patterns of forest loss and fragmentation are rapidly changing. In recent decades, large-scale deforestation has mainly occurred in the southern and eastern portions of the Amazon — in the Brazilian states of Pará, Maranho, Rondônia, Acre, and Mato Grosso, and in northern Bolivia. While rates of forest loss remain very high in these areas, the development of major new highways is providing direct conduits into the heart of the Amazon. If future trends follow past patterns, land-hungry settlers and loggers may largely bisect the forests of the Amazon Basin.The third phenomenon is that climatic variability is interacting with human land uses, creating additional impacts on forest ecosystems. The 1997/98 El Niño drought, for example, led to a major increase in forest burning, with wildfires raging out of control in the northern Amazonian state of Roraima and other locations. Logging operations, which create labyrinths of roads and tracks in forsts, are increasing fuel loads, desiccation and ignition sources in forest interiors. Forest fragmentation also increases fire susceptibility by creating dry, fire-prone forest edges.Finally, recent evidence suggests that intact Amazonian forests are a globally significant carbon sink, quite possibly caused by higher forest growth rates in response to increasing atmospheric CO₂ fertilization. Evidence for a carbon sink comes from long-term forest mensuration plots, from whole-forest studies of carbon flux and from investigations of atmospheric CO₂ and oxygen isotopes. Unfortunately, intact Amazonian forests are rapidly diminishing. Hence, not only is the destruction of these forests a major source of greenhouse gases, but it is reducing their intrinsic capacity to help buffer the rapid anthropogenic rise in CO₂.