国家环境保护模范城市现状分析及展望

改革开放以来,在我国经济快速发展的同时,产生了巨大的环境问题,为此,原国家环保总局审时度势,开展了创建国家环境保护模范城市的活动。本文对“创模”考核指标的调整情况进行概括,从总体情况、所属行政区、所属行政区级别、面积4个方面对目前“环保模范”城市状况进行了深入地分析,并对日后的“创模”工作进行了展望。     

企业粉尘职业危害监督量化分级管理指标体系的筛选

目的是建立粉尘职业卫生监督量化分级管理指标体系,为企业的粉尘职业危害防治提供依据。对象与方法是以59名国内职业卫生领域专家为研究对象。通过编制的调查表,让专家依据各指标的重要程度打分。国内职业卫生领域符合条件的专家以电子邮件方式发送调查问卷70份,收回调查表59份。利用变异系数法,计算每一指标的平均分、变异系数和满分比,评价信度和效度,最终筛选确定粉尘企业职业危害监督量化分级管理的指标体系。结果如下,59名专家积极系数84.3%,8项一级指标和69项二级指标的平均值均超过2.5,变异系数均在50%以下,满分比均介于0-1之间,8项一级指标的克朗巴赫值均大于0.6,相关系数介于0.249-0.705之间。除预防措施和粉尘危害项目存在的问题外,其余均具有统计学意义。结论为企业粉尘职业危害监督量化分级管理指标体系经筛选包括8项一级指标和69项二级指标。     

基于GA优化算法的平遥县大气环境质量评价研究

本文以平遥县城及周边区域环境空气质量监测结果为依据,采用具有全局优化的遗传算法（GA）对平遥县及周边区域大气环境质量污染现状进行评价。用大气污染危害指数的普适公式和参数优化后的污染危害指数公式确定污染危害指数的取值范围与评价级别之间的对应关系,由此得出较符合平遥县及周边区域实际的评价结果,同时针对具体情况对此结果进行简单的分析。     

中部六省区制造业地理集中度分析

中部六省区在我国经济社会发展格局中占据重要地位.采用Eillison和Glaser设计的公式计算了中部六省区制造业17个行业从2007-2010年间的地理集中指数.通过对地理集中指数的分析发现,中部六省区制造业存在“小范围小幅度集中,大范围大幅度扩散”现象,且集中主要存在于化学纤维制造业、化学原料及化学制品制造业和专用设备制造业这些技术和资本密集型行业.从制造业17个行业扩散大于集中的现象可见,中部六省区目前还处于聚集效应的下降阶段.     

河北省县域可持续发展评价指标体系的建立与应用研究

以可持续发展相关理论为基础,利用系统工程的理论和方法建立了河北省县域可持续发展指标体系及其评价标准,并在全省范围应用,对全省136个县(市)可持续发展能力做出了评价。结合评价结果,阐述了河北省县域可持续发展能力的特征,提出了河北省县域可持续发展的方向和思路。     

“十一五”期间辽河铁岭段及支流水质变化趋势

用Daniel的Spearman秩相关系数法对辽河铁岭段及支流在"十一五"期间水质进行趋势检验,确定各监测断面超标污染物及综合污染指数上升或下降趋势的显著性,结合铁岭市"十一五"期间的污染治理措施,分析各项污染物产生变化的原因,提出了今后的工作方向,为进一步加强环境管理提供了依据。     

Using simulation to explore the functional relationships of terrestrial carnivore population indices

Population indices based on visits to detection stations commonly are used to monitor wildlife populations. Inferences about populations are based on 1 of 2 measures: (1) change in the proportion of stations visited at least once or (2) change in the cumulative number of visits by unique individuals. The functional relationships between index responses and population density is poorly understood and can lead to misinterpretation of index data when an incorrect functional relationship (e.g. linear) is assumed. We created a flexible simulation environment to study the response of detection-based population indices under a wide variety of conditions meant to reflect species life history and study design. Proportional indices exhibited non-linear saturating responses to changes in population density while cumulative indices responded linearly. Shapes of responses were functions of home range sizes, individual detection probabilities, and spatial arrangement of animals and sampling stations. Non-linear relationships of proportional indices lead to under-estimation of mean population density when data are aggregated from multiple detection stations deployed in a heterogeneous landscape. Cumulative indices have significant statistical advantages over proportional indices including smaller sample sizes required to detect density change, linearity, consistent index responses across a wide range of densities, and ability to aggregate data to meet minimum sample size requirements. Our simulation provides a flexible tool for the interpretation of station-based population indices.     

Estimating dyad association probability under imperfect and heterogeneous detection

In animal behaviour studies, association indices estimate the proportion of time two individuals (i.e. a dyad) spend in association. In terms of dyads, all association indices can be interpreted as estimators of the probability that a dyad is associated. However, traditional indices rely on the assumptions that the probability to detect a particular individual (p) is either approximately one and/or homogeneous between associated and not associated individuals. Based on marked individuals we develop a likelihood based model to estimate the probability a dyad is associated (ψ) accounting for p < 1 and possibly varying between associated and not associated individuals. The proposed likelihood based model allows for both individual and dyadic missing observations. In addition, the model can easily be extended to incorporate covariate information for modeling p and ψ. A simulation study showed that the likelihood based model approach yield reasonably unbiased estimates, even for low and heterogeneous individual detection probabilities, while, in contrast, traditional indices showed moderate to strong biases. The application of the proposed approach is illustrated using a real data set collected from a population of Commerson's dolphin (Cephalorhynchus commersonii) in Patagonia Argentina. Finally, we discuss possible extensions of the proposed model and its applicability in animal behaviour and ecological studies.     

Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.     

基于氮磷指数的小流域氮磷流失风险评价

识别区域氮磷流失综合风险分布状况并对氮磷流失进行综合调控是控制非点源污染的有效措施。但传统的研究往往局限于氮或磷流失风险的单独评估和调控,以密云水库沿湖集约化农区东庄小流域为例,应用氮指数、磷指数及氮磷综合指数法,对区域氮磷流失风险进行综合评价。结果表明：流域氮、磷流失风险总体上较小,80%以上的区域均处于氮、磷流失的无风险或低风险区,但氮、磷流失的空间分布存在较大差异。其中氮流失的高度风险区集中在山地中土壤侵蚀指数较大的果园;而磷流失的高风险区域主要分布在河流沿岸的农业用地。氮磷综合风险指数显示,93.1%的区域处于无风险和低风险区,中度以上风险区占总面积的6.9%,主要集中在流域中部有着较高的肥料施用、地势陡峭且处在河流沿岸的农业用地或山地中。单独考虑氮指数或磷指数都难以反映区域氮磷流失的综合风险状况,容易忽略磷指数高氮指数低、氮指数高和磷指数低以及氮、磷风险在中等的区域。因此,在氮、磷流失风险评估基础上,进行氮磷流失风险的综合评价,可为氮磷流失的综合调控提供指导。