The road map for green development of Western China

China has made some remarkable achievements in sustainable development,but the constant deterioration of the overall trend of the environment has not yet been effectively curbed.To achieve the goal of sustainable development,we must first ensure coordination and coherence of national development goals in different areas and adhere to green development road.To achieve the objectives related to green development,and to clarify the direction of green development in the next 20years,a road map is needed to guide and coordinate the process.This paper describes the concept of the green development road map,introduces a green development road map for the western region,and further elaborates it The road map clarifies the objectives and guiding principles of green development in the western region,points the areas that deserve more concern and institutional innovation,and builds a green development monitoring and evaluation(M&E)framework.Finally,the paper provides corresponding policy recommendations based on the established road map.     

Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.     

A phenomenological model without dispersal kernel to model species migration

Phenomenological approaches to model species migration are usually based on kernel-based methods. These methods require a good knowledge of the dispersal agent behaviour for a given species. They also calculate the location of individuals independently to each other (except the mother plant) and then suppress some of them according to additional interactions such as competition, facilitation and recruitment. In this paper, we propose to use a new phenomenological method, the Gibbs method, to model tree species migration at large scale. The Gibbs method handles the location of adult individuals in terms of pairwise interactions described by a potential function. This function summarizes the set of known and unknown factors determining the spatial distribution of the individuals (or cohorts). The principle of the Gibbs method is to minimize the sum of all pairwise interactions, also called the cost function, in order to optimize the spatial point pattern according to the chosen potential function.     

辽宁省煤矿瓦斯赋存构造控制规律与瓦斯分带划分

瓦斯赋存规律认识不清,是导致煤矿瓦斯突出灾害频发的根本原因。应用瓦斯赋存地质构造逐级控制理论,结合辽宁省煤矿瓦斯地质图编制资料,分析了全省瓦斯赋存构造控制规律,进行了瓦斯分带划分。研究结果表明:印支运动(主幕),在形成一系列EW向、NE向和NW向断裂褶皱带过程中,煤体破坏,形成构造煤。燕山运动,形成了一系列NNE向、NE向的褶皱和逆冲推覆构造,与EW向构造相叠加,构造应力集中,有利于瓦斯突出;同时,岩浆侵入煤系地层使煤变质程度增高,生烃能力增强。喜马拉雅运动时期,挤压作用逐步被拉张取代,拉张裂隙作用有利于瓦斯部分释放。将辽宁省煤矿瓦斯赋存分布划分4个高突瓦斯带,即阜新-铁岭高突瓦斯带、抚顺-沈北高突瓦斯带、北票-南票高突瓦斯带和红阳-本溪高突瓦斯带。     

Global protected-area coverage and human pressure on tidal flats

Tidal flats are a globally distributed coastal ecosystem important for supporting biodiversity and ecosystem services. Local to continental-scale studies have documented rapid loss of tidal habitat driven by human impacts, but assessments of progress in their conservation are lacking. With an internally consistent estimate of distribution and change, based on Landsat satellite imagery, now available for the world's tidal flats, we examined tidal flat representation in protected areas (PAs) and human pressure on tidal flats. We determined tidal flat representation and its net change in PAs by spatially overlaying tidal flat maps with the World Database of Protected Areas. Similarly, we overlaid the most recent distribution map of tidal flats (2014–2016) with the human modification map (HM_c) (range from 0, no human pressure, to 1, very high human pressure) to estimate the human pressure exerted on this ecosystem. Sixty-eight percent of the current extent of tidal flats is subject to moderate to very high human pressure (HM_c > 0.1), but 31% of tidal flat extent occurred in PAs, far exceeding PA coverage of the marine (6%) and terrestrial (13%) realms. Net change of tidal flat extent inside PAs was similar to tidal flat net change outside PAs from 1999 to 2016. Substantial shortfalls in protection of tidal flats occurred across Asia, where large intertidal extents coincided with high to very high human pressure (HM_c > 0.4–1.0) and net tidal flat losses up to 86.4 km² (95% CI 83.9–89.0) occurred inside individual PAs in the study period. Taken together, our results show substantial progress in PA designation for tidal flats globally, but that PA status alone does not prevent all habitat loss. Safeguarding the world's tidal flats will thus require deeper understanding of the factors that govern their dynamics and effective policy that promotes holistic coastal and catchment management strategies.     

Bayes Estimation of a Distribution Function Using Ranked Set Samples

Aranked set sample (RSS), if not balanced, is simply a sample of independent order statistics gener- ated from the same underlying distribution F. Kvam and Samaniego (1994) derived maximum likelihood estimates of F for a general RSS. In many applications, including some in the environ- mental sciences, prior information about F is available to supplement the data-based inference. In such cases, Bayes estimators should be considered for improved estimation. Bayes estimation (using the squared error loss function) of the unknown distribution function F is investigated with such samples. Additionally, the Bayes generalized maximum likelihood estimator (GMLE) is derived. An iterative scheme based on the EM Algorithm is used to produce the GMLE of F. For the case of squared error loss, simple solutions are uncommon, and a procedure to find the solution to the Bayes estimate using the Gibbs sampler is illustrated. The methods are illustrated with data from the Natural Environmental Research Council of Great Britain (1975), representing water discharge of floods on the Nidd River in Yorkshire, England     

A multicriteria approach for mapping risks of agricultural pollution for water resources: The Venice Lagoon watershed case study

A multicriteria analysis system was developed for producing risk maps of agricultural pollution due to alternative cultivation systems in the Watershed of the Lagoon of Venice (WLV) in Italy. Results of a field-scale simulation model for agricultural diffuse pollution were used to compile a matrix of environmental impacts, in terms of pollution indices. The most widespread combinations of typical environments (as defined by combinations of soil and climate variables) and alternative land uses (types of crops and cultivation systems) were described in the impact matrix. Land use in terms of crop distribution was based on census data. Two alternative cultivation systems were defined on the basis of the recent changes to the European Common Agricultural Policy: ordinary and eco-compatible. The effects of alternative scenarios were evaluated in terms of pollution risks for water resources. The evaluation procedure was built into the framework of a geographical information system to take into account the spatial features of pollution phenomena, vulnerability of the land and risk for water resources. The results demonstrated the great potential of eco-compatible practices for reducing the risks for surface and groundwater (−15 and −50%, respectively).     

工业噪声对脑认知影响的功率谱估计分析

为明确工业噪声对大脑认知的负面影响,采集火电厂球磨机的工业噪声,征集10名在校大学生在90 dB(A)的稳态工业噪声状态下及安静状态下的32导联脑电图(EEC).采用傅里叶变换方法进行频域分析,提取δ频段、θ频段、α1频段、α2频段和β频段功率谱,并进行统计分析.结果 表明:噪声环境与静音环境相比较,被试在δ频段、θ频...     

Assessing agricultural vulnerability to climate change in the Nordic countries – an interactive geovisualization approach

Nordic agriculture must adapt to climate change to reduce vulnerability and exploit potential opportunities. Integrated assessments can identify and quantify vulnerability in order to recognize these adaptation needs. This study presents a geographic visualization approach to support the interactive assessment of agricultural vulnerability to climate change. We have identified requirements for increased transparency and reflexivity in vulnerability assessments, arguing that these can be met by geographic visualization. A conceptual framework to support the integration of geographic visualization for vulnerability assessments has been designed and applied for the development of AgroExplore, an interactive tool for assessing agricultural vulnerability to climate change in Sweden. To open up the black box of composite vulnerability indices, AgroExplore enables the user to select, weight, and classify relevant indicators into sub-indices of exposure, sensitivity, and adaptive capacity. This enables the exploration of underlying indicators and factors determining vulnerability in Nordic agriculture.