Variations in the chemical properties of landfill leachate

Landfill leachates were collected and their chemical properties analyzed once every two months over a ten-month period from the Gin Drinkers' Bay (GDB) and Junk Bay (JB) landfills. The contents of solids, and inorganic and organic components fluctuated considerably with time. In general, the chemical properties of the two leachates correlated negatively (P<0.05) with the amounts of rainfall prior to the sampling periods. However, magnesium and pH of the leachates remained relatively constant with respect to sampling time. The JB leachate contained higher average contents of solids and inorganic and organic matter than those of GDB with the exception of trace metals. Trace metals were present in the two leachates in trace quantities (<1.0 mg/liter). The concentrations of average ammoniacal nitrogen were 1040 and 549 mg/liter, while chemical oxygen demand (COD) values were 767 and 695 mg/liter for JB and GDB leachates, respectively. These results suggest that the leachates need further treatment before they can be discharged to the coastal waters.     

Classification and spatial mapping of riparian habitat with applications toward management of streams impacted by nonpoint source pollution

Management of riparian habitats has been recognized for its importance in reducing instream effects of agricultural nonpoint source pollution. By serving as a buffer, well structured riparian habitats can reduce nonpoint source impacts by filtering surface runoff from field to stream. A system has been developed where key characteristics of riparian habitat, vegetation type, height, width, riparian and shoreline bank slope, and land use are classified as discrete categorical units. This classification system recognizes seven riparian vegetation types, which are determined by dominant plant type. Riparian and shoreline bank slope, in addition to riparian width and height, each consist of five categories. Classification by discrete units allows for ready digitizing of information for production of spatial maps using a geographic information system (GIS). The classification system was tested for field efficiency on Tom Beall Creek watershed, an agriculturally impacted third-order stream in the Clearwater River drainage, Nez Perce County, Idaho, USA. The classification system was simple to use during field applications and provided a good inventory of riparian habitat. After successful field tests, spatial maps were produced for each component using the Professional Map Analysis Package (pMAP), a GIS program. With pMAP, a map describing general riparian habitat condition was produced by combining the maps of components of riparian habitat, and the condition map was integrated with a map of soil erosion potential in order to determine areas along the stream that are susceptible to nonpoint source pollution inputs. Integration of spatial maps of riparian classification and watershed characteristics has great potential as a tool for aiding in making management decisions for mitigating off-site impacts of agricultural nonpoint source pollution.     

中国区域能源效率对霾污染的空间效应——基于空间杜宾模型的实证分析

以中国29个省市自治区为研究对象，并以2001~2015年PM_2.5年度均值作为衡量霾污染的指标，构建了全要素能源效率对中国霾污染影响的空间杜宾模型.研究表明：中国绝大部分省市的霾污染演变趋势具有倒"U"的特点，这说明中国的霾污染在2001~2015年间经历了先上升后下降的总体变化趋势；无论是采用地理相邻权重还是经济权重，中国省际间的霾污染均存在显著空间集聚效应，这揭示霾污染的治理与控制需要地区间建立恰当的合作机制，避免出现"损人利己"现象；在对一系列经济社会因素控制的基础上，全要素能源效率对霾污染存在显著的负向影响并且具有显著的空间溢出效应，这表明全要素能源效率的提高不仅会显著降低本地区的霾污染水平，还将对周边地区的霾污染产生显著的负向影响；分解方程结果显示，技术进步和技术效率对霾污染的负向效应显著，并且技术进步的直接效应大于技术效率，但两者的间接效应均显著大于直接效应，这揭示了技术扩散对霾污染具有重要的潜在促降效应.因此，在治理霾污染的过程中，要通过不断优化能源结构和开发新能源技术以发挥全要素能源效率的减霾效果，以及通过地区间的交流与合作发挥技术扩散效应在霾污染治理中的作用.     

Community richness and stability in agricultural landscapes: The importance of surrounding habitats

In this paper, the role played by habitat diversity in the landscape on species richness and on the stability of farmland bird communities was investigated. Species richness was estimated on 374 samples monitored in farmland by the French breeding bird survey during the 2001–2005 period. A capture–recapture approach was used to estimate species richness accounting for the variation in detection probability among species of the 100 most common species detected in farmland. Landscape structure and composition were measured both in farmland and in adjacent habitats. The independent effect of each variable on community richness and stability was further assessed using hierarchical variance partitioning and taking spatial autocorrelation into account. A strong matrix effect was detected: non-cropped land deeply influenced richness and stability of bird assemblages.     

Soil seed bank and seedling populations of Hordeum murinum and Cardaria draba in saffron fields

Geostatistical techniques were used to characterize the spatial relationship between Hordeum murinum and Cardaria draba seedling and soil seed bank over the entire growing season of 2004–2005 in three saffron (Crocus sativus) fields, located in Southern Khorasan (33°N latitude, 57°E longitude), Iran. The maps of H. murinum seed bank density corresponded moderately to those seedling density in a and strongly to those in b and c fields. The emergence percentage of C. draba was higher than for H. murinum in all fields. Semivariograms showed spatial autocorrelation in seed bank and seedling populations of H. murinum and C. draba in all fields. Grey-scale field maps of C. draba seed banks corresponded visually to maps of seedling populations and could have been used to target control efforts, but visual correspondence between H. murinum seed bank and seedling maps was poor.     

南方红豆杉天然群落种群空间分布规律的研究

采用罗盘仪定位测定的方法，对井冈山较大的南方红豆杉天然群落的空间分布规律进行研究，结果表明：群落的南方红豆杉种群累积曲线呈Ｓ型，具有Ｓ型曲线的特性，群落内部南方红豆杉的个体分布格局表现出空间和时间上的规律。     

Spatial heterogeneity of vegetation under different grazing intensities in the Northwest Heilongjiang Steppe of China

In northern China, recent degradation of semi-arid grasslands caused by overgrazing is serious. At Green Grassland Village, west of Daqing City in Heilongjiang Province, three grasslands with different degrees of degradation were surveyed. In the survey, a new method of vegetation analysis based on the beta-binomial distribution, was adopted to describe the frequency of occurrence and spatial heterogeneity for each plant species.Chenopodium centrorubrum, Cleistogenes squarrosa and a Carex species were commonly found in the three grasslands. Non-degraded grassland with light grazing was dominated by Arundinella hirta, Filifolium sibiricium, Lespedeza hedysaroides, Potentilla verticillaris, Stipa baicalensis and Thymus serpyllum. These species belong to the erect-type or do not develop large clones under light grazing. These species can be used as indicators of a well-managed grassland in this area. On the other hand, the degraded grassland with heavy grazing was dominated by Artemisia spp. and Euphorbia humifusa var. pilosa. Artemisia spp., E. humifusa var. pilosa, Eragrostis pilosa and Setaria viridis belong to the creeping-type or develop rather large stocks or stolons under heavy utilization. These species increased the spatial heterogeneity in the degraded grassland with heavy grazing.Under degraded conditions, species diversity decreased and spatial heterogeneity of the communities tended to increase.     

A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague's environment extended by LIDAR data

Environmental pollution of urban areas is one of key factors that state authorities and local agencies have to consider in the decision-making process. To find a compromise among many criteria, spatial analysis extended by geostatistical methods and dynamic models has to be carried out. In this case, spatial analysis includes processing of a wide range of air, water and soil pollution data and possibly noise assessment and waste management data. Other spatial inputs consist of data from remote sensing and GPS field measurements. Integration and spatial data management are carried out within the framework of a geographic information system (GIS). From a modeling point of view, GIS is used mainly for the preprocessing and postprocessing of data to be displayed in digital map layers and visualized in 3D scenes. Moreover, for preprocessing and postprocessing, deterministic and geostatistical methods (IDW, ordinary kriging) are used for spatial interpolation; geoprocessing and raster algebra are used in multi-criteria evaluation and risk assessment methods. GIS is also used as a platform for spatio-temporal analyses or for building relationships between the GIS database and stand-alone modeling tools. A case study is presented illustrating the application of spatial analysis to the urban areas of Prague. This involved incorporating environmental data from monitoring networks and field measurements into digital map layers. Extra data inputs were used to represent the 3D concentration fields of air pollutants (ozone, NO₂) measured by differential absorption LIDAR. ArcGIS was used to provide spatial data management and analysis, extended by modeling tools developed internally in the ArcObjects environment and external modules developed with MapObjects. Ordinary kriging methods were employed to predict ozone concentrations in selected 3D locations together with estimates of variability. Higher ozone concentrations were found above crossroads with their heavy traffic than above the surrounding areas. Ozone concentrations also varied with height above the digital elevation model. Processed data, spatial analysis and models are integrated within the framework of the GIS project, providing an approach that state and local authorities can use to address environmental protection issues.     

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.     

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.