Land Cover Changes as a Result of Environmental Restrictions on Nitrate Leaching in Dairy Farming

Nitrate leaching forms an important environmental problem because it causes pollution of groundwater and surface water, and adds to already problematic eutrophication. This study analyses the impact of reductions in nitrate leaching on land cover decisions of dairy farms, of which the activities make an important contribution to nitrate leaching. As the level of nitrate leaching depends on groundwater depth as well as on the supply of nitrogen, spatial variation in groundwater levels will cause a spatial variation in land cover under restrictions on nitrate leaching. A non-linear partial optimisation model for the economic and ecological aspects of the problem were used to show how land cover and dairy farms' financial balances change when nitrate losses are reduced. The model is spatially explicit, and describes nitrate leakage and yields of maize and grass as a function of groundwater depth, including the effects of various grazing systems. The model analyses the decisions of a risk neutral agent who minimises costs under the following constraints: (i) production, feed requirements and mass balances for fodder; (ii) constraints for nitrate leaching. Economic costs are attributed to increased costs of fodder and processing of manure when nitrate restrictions are tightened. An important result of the study is the variation in compliance costs and land cover for maize and grass production brought about by spatial variation in groundwater depth. While the effects are negligible for some shallow groundwater classes, it is extremely difficult in other classes – if not impossible – to obtain the EU standard of maximum admissible losses of 34 kg N ha^–1 at low costs. The study shows an important reduction in land cover by maize.     

Soil and vegetation carbon stocks in Brazilian Western Amazonia: relationships and ecological implications for natural landscapes

The relationships between soils attributes, soil carbon stocks and vegetation carbon stocks are poorly know in Amazonia, even at regional scale. In this paper, we used the large and reliable soil database from Western Amazonia obtained from the RADAMBRASIL project and recent estimates of vegetation biomass to investigate some environmental relationships, quantifying C stocks of intact ecosystem in Western Amazonia. The results allowed separating the western Amazonia into 6 sectors, called pedo-zones: Roraima, Rio Negro Basin, Tertiary Plateaux of the Amazon, Javari-Juruá-Purus lowland, Acre Basin and Rondonia uplands. The highest C stock for the whole soil is observed in the Acre and in the Rio Negro sectors. In the former, this is due to the high nutrient status and high clay activity, whereas in the latter, it is attributed to a downward carbon movement attributed to widespread podzolization and arenization, forming spodic horizons. The youthful nature of shallow soils of the Javari-Juruá-Purus lowlands, associated with high Al, results in a high phytomass C/soil C ratio. A similar trend was observed for the shallow soils from the Roraima and Rondonia highlands. A consistent east–west decline in biomass carbon in the Rio Negro Basin sector is associated with increasing rainfall and higher sand amounts. It is related to lesser C protection and greater C loss of sandy soils, subjected to active chemical leaching and widespread podzolization. Also, these soils possess lower cation exchangeable capacity and lower water retention capacity. Zones where deeply weathered Latosols dominate have a overall pattern of high C sequestration, and greater than the shallower soils from the upper Amazon, west of Madeira and Negro rivers. This was attributed to deeper incorporation of carbon in these clayey and highly pedo-bioturbated soils. The results highlight the urgent need for refining soil data at an appropriate scale for C stocks calculations purposes in Amazonia. There is a risk of misinterpreting C stocks in Amazonia when such great pedological variability is not taken into account.     

Landscape Correlates of Breeding Bird Richness Across the United States Mid-Atlantic Region

Using a new set of landscape indicator data generated by the U.S.EPA, and a comprehensive breeding bird database from the National Breeding Bird Survey, we evaluated associations between breeding bird richness and landscape characteristics across the entire mid-Atlantic region of the United States. We evaluated how these relationships varied among different groupings (guilds) of birds based on functional, structural, and compositional aspects of individual species demographics. Forest edge was by far the most important landscape attribute affecting the richness of the lumped specialist and generalist guilds; specialist species richness was negatively associated with forest edge and generalist richness was positively associated with forest edge. Landscape variables (indicators) explained a greater proportion of specialist species richness than the generalist guild (46% and 31%, respectively). The lower value in generalists may reflect finer-scale distributions of open habitat that go undetected by the Landsat satellite, open habitats created by roads (the areas from which breeding bird data are obtained), and the lumping of a wide variety of species into the generalist category. A further breakdown of species into 16 guilds showed considerable variation in the response of breeding birds to landscape conditions; forest obligate species had the strongest association with landscape indicators measured in this study (55% of the total variation explained) and forest generalists and open ground nesters the lowest (17% of the total variation explained). The variable response of guild species richness to landscape pattern suggests that one must consider species' demographics when assessing the consequences of landscape change on breeding birds.     

星形大分子液晶在气相色谱分析中的应用研究

将首次合成的偶氮苯硅氧烷类星形液晶大分子化合物用于毛细管柱气相色谱固定液 ,在其相转变温度附近具有良好的选择性 ,成功地分离了二甲酚异构体。     

秸秆焚烧对北京市空气质量的影响

用火焰原子吸收光谱法测试了北京市两个采样点 1 #站 (十三陵站 ,清洁对照点 )和 5#站 (天坛站 ,居民生活区 )1 1 0个大气颗粒物样品中的水溶性钾 ,以表征秸秆焚烧颗粒物。 1 #站水溶性钾的质量浓度年均值为 1 .2 1 μg/m3,其中以1 998年 6月份浓度 (3 .0 7μg/m3)最高 ,是 5月份 (1 .0 2μg/m3)的 3倍 ;5#站水溶性钾的质量浓度年均值为 1 .94μg/m3,6月份 (4 .2 2 μg/m3)最高 ,是 5月份 (1 .97μg/m3)的 2 .1倍。数据分析结果表明 1 998年 6月份麦收季节存在以秸秆焚烧为主的生物质燃烧现象 ,使大气颗粒物中有机碳浓度水平升高 ,并对北京市的空气质量带来负面影响     

The Need for Scale Sensitive Approaches in Spatially Explicit Land Use Change Modeling

This paper introduces some of the issues that are relevant to the spatially explicit modeling of land use systems. A short overview is given of the ways and means in which a number of different land use change models describe the land use system. Specific attention is given to the empirical modeling approach used in the CLUE (Conversion of Land Use and its Effects) modeling framework. This approach is demonstrated for three case studies in China, Ecuador and the Atlantic Zone of Costa Rica. These case-studies illustrate the methodology for multi-scale analysis of land use driving factors and their application in spatially explicit modeling exercises. Model functioning, performance and limitations are discussed. The presented case-studies indicate that empirical modeling results can contribute to a better theoretic imbedding of land use change research in scale sensitive and integrated theories.     

Land Use Changes in Regional Economic Theory

Land use and land cover changes are a major source of environmental degradation and therefore a serious issue in sustainable development studies and in the integrated assessment of environmental problems. In an attempt to understand part of the complexity of land use change we here aim to explore the ways in which economists deals with the land use issue. We argue that space is one of the forgotten items in economics. Economists often seem to ignore high-resolution spatial dynamics either because they are not considered as an important feature of the problem or out of habit. It seems as though there is a trade-off between spatial resolution and human behaviour in current applications. Certain types of models are capable of capturing the spatial complexity of urban and regional areas, for instance, by using cellular automata. Other types of land use models clearly have shortcomings where it concerns spatial detail. Dynamic modelling approaches do not guarantee a high spatial resolution.We propose to give more attention to agent based modelling as this type of modelling provides a specific connection between processes on micro level and macro-level spatial structures.     

Extension of the EUROS Integrated Air Quality Model to Fine Particulate Matter by Coupling to CACM/MADRID 2

The European Operational Smog (EUROS) integrated air quality modelling system has been extended to model fine particulate matter (PM). From an extended literature study, the Caltech Atmospheric Chemistry Mechanism and the Model of Aerosol Dynamics, Reaction, Ionisation and Dissolution were selected and recently coupled to EUROS. Currently, modelling of mass and chemical composition of aerosols in two size fractions (PM_2.5 and PM_10–2.5) is possible. The chemical composition is expressed in terms of seven components: ammonium, nitrate, sulphate, elementary carbon, primary inorganic compounds, primary organic compounds and secondary organic compounds. Calculated PM₁₀ concentrations and chemical composition are presented for two summer months of the year 2003 (1 July to 31 August).     

Principal response curves technique for the analysis of multivariate biomonitoring time series

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrated.