三峡工程生态环境影响分析

经过六年多的研究,笔者认为:三峡工程对生态环境的影响有有利影响和不利影响两个方面。对不利影响只要认真对待,采取措施,可以减少到最小程度,所以生态环境问题不是三峡工程决策的制约因素。     

城镇用地扩张的影响分析

社会经济条件的变化是推动城镇用地扩张的重要力量。城镇用地规模不断扩大，不仅促进了城镇本身功能的完善、经济实力的提高，带动了周围农村地区社会经济的发展。而且，城镇用地大量、快速向外扩张也给城镇本身及其周围农村带来了较大的负面影响。     

新排污收费条例对燃煤电厂的影响探析

《排污费征收和管理条例》(简称新《条例》)的实施是我国排污收费制度的重大变革，对相关行业将产生不同程度的影响。着重分析了新《条例》的实施对燃煤电厂的不利影响，以及对企业增加环保投入治理污染的推动作用。     

造纸行业环境影响评价中清洁生产评价的实践与思考

造纸行业是资金密集型、技术密集型产业,企业能耗、水耗、资源消耗很大.随着产品产量的增加,行业污染物排放量势必存在增长趋势,造纸行业清洁生产潜力巨大,造纸项目环境影响评价中的清洁生产水平评价变得越来越重要.就造纸行业环境影响评价中清洁生产水平评价的评价指标、评价方法、评价工作中遇到的实际问题以及当前形势对环境影响评价的更高要求以及应对办法等进行了论述.     

采动对矿区生态环境的影响及治理对策

根据开采沉陷学的原理,对由于采动而引起的地质灾害及表现形式作了详细阐述,以及由此对矿区生态环境所产生的影响进行了分析评价,并提出了相应的治理措施。     

The importance of agricultural yield elasticity for indirect land use change: a Bayesian network analysis for robust uncertainty quantification

ABSTRACT

A major barrier to realising biofuels’ climate change mitigation potential is uncertainty concerning carbon emissions from indirect land use change (ILUC). Central to this uncertainty is the extent to which yields can respond dynamically to increased demand for agricultural commodities. This study examines the elasticity of soybean and corn yields in the USA for 1990–2017 using Bayesian network models to robustly quantify uncertainty. The central finding is that a single parameter value for yield elasticity does not adequately represent the effects of technology, policy and price pressures through time. The models demonstrate the limiting role of technological progress as well as farmers’ capital investment in response to system shocks. Results suggest evaluation of parameter uncertainty alone is unlikely to capture a full range of future ILUC scenarios and reiterate the need for ILUC studies to use probabilistic approaches as standard to robustly inform climate change mitigation policies.     

环境中内分泌干扰物质的研究概述

在总结最新文献的基础上，就环境中的内分泌干扰物质的概念，研究背景，研究方法，在环境中的分布情况作了简单综述和介绍，并对有待开展的研究提出了建议，为国内在环境内分泌干扰物方面的研究提供参考。     

Isomorphic chain graphs for modeling spatial dependence in ecological data

Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation, their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating correlational structure to account for observations collected through time and/or space—features of most ecological data—have not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph (ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network is shown with an ICG model.     

Social influence on the correlation between behaviours in young-of-the-year perch

The connection between risk-taking behaviour and exploratory behaviour in young-of-the-year perch (Perca fluviatilis) was studied in aquarium experiments to see whether individual behaviour patterns could be identified in this species and also to investigate how individual behaviour is influenced by their social environment. Risk-taking was defined as the time spent foraging in an open area vs hiding in the vegetation in the presence of a piscivore. Explorative behaviour was measured as latency to enter a passage leading to an unknown area. Groups of four fish were used for the observations, and both behaviours measured were positively correlated with the mean scores of these behaviours in the other group members. Risk-taking and explorative behaviours were correlated only when data was adjusted for the behaviour of the other group members. Individuals that spent more time in the open than their companions also tended to be faster than the others to enter the passage to the unknown area and vice versa. The results indicate that there are consistent individual differences in boldness in perch, but also that behaviour could be modified according to the behaviour of group members.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.