专家笔谈：资源环境热点问题

重大的资源开发利用工程建设设想或实施往往容易引起社会各界的广泛关注。如何正确认识和科学评价各种资源环境热点问题是大家讨论的重点。文章认为任何重大资源开发工程必须遵循自然规律、生态规律、资源规律和经济规律,必须结合国家战略需求和地缘政治关系,开展前期深入的系统性综合调查,进行生态安全与资源工程安全评价和交叉学科论证,并对其科学价值、社会价值和经济价值等做出正确的判断。建议：未来的资源环境工程建设要谨防“大跃进”,要科学认识自然资源系统规律,守护好美丽的地球家园;要切实履行资源科学家的社会责任,提倡基于科学研究和科学态度的学术争鸣,科学问题最好还是由科学家去讨论;一些争议大的科研课题慎用公益性研发经费来支持。     

大气自净能力指数的气候特征与应用研究

为了定量地评估污染气象条件对空气污染的作用并实现对空气污染潜势的预报,本文在城市大气污染数值预报系统（CAPPS）预报原理的基础上,定义了大气自净能力指数,并分别给出了采用气象站观测资料和通过数值模拟计算大气自净能力指数的方法.基于气象站观测资料的全国大气自净能力指数分析计算表明,全国大气自净能力最差的地区分布在四川盆地和新疆塔里木盆地,大气自净能力最强的地区分布在青藏高原、蒙古高原、云贵高原、以及东北平原和三江平原、山东半岛和海南岛;1961~2017年,京津冀、长三角和珠三角地区的大气自净能力指数呈下降的变化趋势,全年低自净能力日数呈上升的变化趋势.采用大气自净能力指数评估2014年北京APEC会议期间大气污染防控效果,表明在11月8~10日极端不利扩散气象条件发生时,减排措施使北京市空气质量AQI平均降低77%,使京津冀平原地区11个城市的空气质量AQI平均降低37%.基于国家气候中心月动力延伸气候预测模式（DERF2.0）的预报产品和中尺度模式（WRF）,建立了可以预测全国未来40d逐日大气自净能力指数的延伸期-月尺度大气污染潜势预测系统,回报实验表明,在大多数情况下可以提前15d预报出大气重污染过程;月尺度的大气重污染过程预报效果更大程度上取决于月动力延伸气候预测模式（DERF2.0）的预报准确率.     

反舰导弹命中精度预测与评估

目的解决反舰导弹命中精度受试验航区及靶场试验环境等客观条件制约,难以通过飞行试验得到客观全面评估的问题。方法在对影响反舰导弹命中精度的主要因素进行分析的基础上,利用实际环境条件下的飞行试验数据,构建基于偏最小二乘回归的命中精度模型,实现对反舰导弹各种使用环境条件命中精度的定量评估。结果在构建基于偏最小二乘回归的命中精度模型后,可以实现对90°发射扇面角、20 m/s侧风以及300 km最大射程等实际试验无法考核的边界使用环境条件下反舰导弹的命中精度进行定量评估。结论通过实例分析表明了方法的有效性和可行性,可以实现有小样本条件下对反舰导弹命中精度的评估。     

可靠性强化试验技术在某型伺服作动器研制中的适用性探索

目的探索可靠性强化试验技术在典型机电液一体化产品伺服作动器研制过程中的适用性。方法以某型伺服作动器为研究对象,从故障激发的角度对可靠性强化试验技术的应用进行可行性分析,在响应调查和应力分析的基础上,结合产品的工作特点设计适用于该类伺服作动器的可靠性强化试验方案,包含低温步进应力试验、高温步进应力试验、快速温度循环试验10个循环、振动步进应力试验(包含气锤式三轴向六自由度超高斯随机振动方式及电磁振动台随机振动方式)及综合环境应力试验5个循环,并依此进行试验。结果在快速温度循环试验及综合环境应力试验过程中,均有效地激发出了产品的漏油故障,与相似产品外场暴露的漏油故障模式相吻合。结论可靠性强化试验技术可有效地应用于典型机电液一体化产品伺服作动器的研制过程,设计的可靠性强化试验方案可有效地激发外场出现频率较高的故障,可作为该类产品研制的试验手段之一。     

典型铅锌矿化区不同土地利用类型土壤重金属污染特征与评价

选取云南者海典型铅锌矿区周边冶炼区（A）、粮食主产区（B）、保护区（C）三个区域土壤为研究对象,分析三个区域内林地（LD）和耕地（GD）土壤pH、总碳（TC）、总氮（TN）、总磷（TP）和5种重金属（Hg、Cd、Pb、Zn、Cu）含量,利用典范对应分析（CCA）研究不同土地利用类型下它们之间的关系,基于系统熵值与重金属生物毒性改进灰色聚类评价法对研究区重金属污染程度进行评价。结果表明,A、B、C三区土壤的5种重金属均超过云南省土壤背景值,且含量A区 > B区 > C区,所有土壤样品Hg、Cd、Pb、Zn、Cu平均含量分别为7.24、1.53、1 794、2 892、210 mg/kg;LD土壤重金属含量普遍高于GD。研究区土壤pH总体呈弱酸性,但A区土壤受矿业活动影响呈弱碱性。TC、TN含量和C/N值均表现出LD大于GD,但TP含量表现为GD显著大于LD （P<0.05）。CCA分析表明LD和GD土壤pH与Cd和Cu呈负相关,与Zn和Pb呈正相关,且pH对重金属含量的影响最大;TC、TP与重金属Cd和Cu在LD土壤中呈正相关,在GD土壤中呈负相关。改进灰色聚类评价结果表明重金属污染程度均表现为LD大于GD;A区污染最严重且均呈重度污染,B区次之,C区污染程度最轻。经比较本文改进的灰色聚类评价法在准确性和灵敏度方面优于传统方法。     

Bi-national assessment of the Great Lakes: SOLEC partnerships

Many administrative jurisdictions have authority over parts of the Great Lakes, sometimes with competing purposes as well as governance at differing scales of time and space. As demand increases for high quality information that is relevant to environmental managers, environmental and natural resource agencies with limited budgets must look to interdisciplinary, collaborative approaches for the collection, analysis and reporting of data. The State of the Lakes Ecosystem Conferences (SOLEC) were begun in 1994 in response to reporting requirements of the Great Lakes Water Quality Agreement between Canada and the U.S. The biennial conferences provide independent, science-based reporting on the state of health of the Great Lakes ecosystem components. A suite of indicators necessary and sufficient to assess Great Lakes ecosystem status was introduced in 1998, and assessments based on a subset of the indicators were presented in 2000. Because SOLEC is a multi-agency, multi-jurisdictional reporting venue, the SOLEC indicators require acceptance by a broad spectrum of stakeholders in the Great Lakes basin. The SOLEC indicators list is expected to provide the basis for government agencies and other organizations to collaborate more effectively and to allocate resources to data collection, evaluation and reporting on the state of the Great Lakes basin ecosystem.     

Land Degradation Monitoring in Namibia: A First Approximation

This paper presents development of a first approximation of a Namibian, national level, land degradation monitoring system. The process involved a large number of stakeholders and led to the definition of four primary indicators that were regarded as related to land degradation in Namibia: population pressure, livestock pressure, seasonal rainfall and erosion hazards. These indicators were calculated annually for the period 1971–1997. Annual land degradation risk maps were produced for the same period by combining the indicators. A time series analysis of results generated by indicators was undertaken at two sites. The analysis revealed a general trend towards an increased land degradation risk over the period 1971–1997. A decrease in annual rainfall and an increase in livestock numbers caused this negative trend at one site, while decreased annual rainfall and increased human population were the causes at a second site. Evaluation of resulting maps through direct field observations and long-term monitoring at selected study sites with different conditions relevant for the indicators defined, is an essential next step.     

官厅水库富营养化的评价

综述了官厅水库水系的近期状况 ;在库区选择了 8个具有代表性的断面 ,分析测试了 1 1项水质指标 ,采用 Vol-lenweider的理论评价了库区水质为中 -富营养等级 ;提出了改善水库水系的 4点建议     

Application of the benthic index of biotic integrity to environmental monitoring in Chesapeake Bay

The Chesapeake Bay benthic index of biotic integrity (B-IBI) was developed to assess benthic community health and environmental quality in Chesapeake Bay. The B-IBI provides Chesapeake Bay monitoring programs with a uniform tool with which to characterize bay-wide benthic community condition and assess the health of the Bay. A probability-based design permits unbiased annual estimates of areal degradation within the Chesapeake Bay and its tributaries with quantifiable precision. However, of greatest interest to managers is the identification of problem areas most in need of restoration. Here we apply the B-IBI to benthic data collected in the Bay since 1994 to assess benthic community degradation by Chesapeake Bay Program segment and water depth. We used a new B-IBI classification system that improves the reliability of the estimates of degradation. Estimates were produced for 67 Chesapeake Bay Program segments. Greatest degradation was found in areas that are known to experience hypoxia or show toxic contamination, such as the mesohaline portion of the Potomac River, the Patapsco River, and the Maryland mainstem. Logistic regression models revealed increased probability of degraded benthos with depth for the lower Potomac River, Patapsco River, Nanticoke River, lower York River, and the Maryland mainstem. Our assessment of degradation by segment and water depth provided greater resolution of relative condition than previously available, and helped define the extent of degradation in Chesapeake Bay.     

Risk-Based Prioritization of Air Pollution Monitoring Using Fuzzy Synthetic Evaluation Technique

Air pollution monitoring programs aim to monitor pollutants and their probable adverse effects at various locations over concerned area. Either sensitivity of receptors/location or concentration of pollutants is used for prioritizing the monitoring locations. The exposure-based approach prioritizes the monitoring locations based on population density and/or location sensitivity. The hazard-based approach prioritizes the monitoring locations using intensity (concentrations) of air pollutants at various locations. Exposure and hazard-based approaches focus on frequency (probability of occurrence) and potential hazard (consequence of damage), respectively. Adverse effects should be measured only if receptors are exposed to these air pollutants. The existing methods of monitoring location prioritization do not consider both factors (hazard and exposure) at a time. Towards this, a risk-based approach has been proposed which combines both factors: exposure frequency (probability of occurrence/exposure) and potential hazard (consequence).This paper discusses the use of fuzzy synthetic evaluation technique in risk computation and prioritization of air pollution monitoring locations. To demonstrate the application, common air pollutants like CO, NO_x, PM₁₀ and SO_x are used as hazard parameters. Fuzzy evaluation matrices for hazard parameters are established for different locations in the area. Similarly, fuzzy evaluation matrices for exposure parameters: population density, location and population sensitivity are also developed. Subsequently, fuzzy risk is determined at these locations using fuzzy compositional rules. Finally, these locations are prioritized based on defuzzified risk (crisp value of risk, defined as risk score) and the five most important monitoring locations are identified (out of 35 potential locations). These locations differ from the existing monitoring locations.