三峡库区笋溪河流域面源污染及其与土壤可蚀性k值的关系

研究三峡库区面源污染特征及其与水土流失的关系,可为库区氮磷污染和土壤侵蚀控制提供依据.选择三峡库区库尾笋溪河流域,在流域内分园地、林地和耕地3种土地利用类型共采集126个土壤样品,并在主干和支流采集52个水质样品.根据EPIC模型计算土壤可蚀性k值,分析流域内土壤可蚀性k值对面源污染的影响.结果表明,笋溪河流域面源污染主要是氮污染,总氮均值达1.37 mg/L,氮素的主要形态为硝态氮,占总氮的71.2%;总磷浓度为0.1 mg/L.流域内土壤可蚀性k值均值为0.040,随着土层加深土壤可蚀性k值呈上升趋势;林地土壤可蚀性k值显著低于园地和耕地.笋溪河流域总氮浓度与园地和耕地0-20 cm土壤可蚀性k值有关,硝态氮浓度与耕地0-40 cm土壤可蚀性k值有关.因此,笋溪河流域面源污染严重,主要来源是耕地和园地,应实行免耕、植物篱等措施,同时减少化肥施用,增加有机肥比例,以增加土壤抗侵蚀能力,进而控制流域水土流失和面源污染.(图6参37)     

Sewage sludge ditch for recovering heavy metals can improve crop yield and soil environmental quality

• Indirect use of sludge in ditches alongside plants was tested in field experiments. • The dried and stabilized sludge in ditches was recovered with heavy metals. • Cd, Pb, Cu and Zn in the planted soil were all in a safe range. • The indirect use of sludge increased plant yield, soil N content and C storage. The treatment and disposal of municipal sewage sludge (MSS) is an urgent problem to be resolved in many countries. Safely using the nutrients within MSS to increase crop yield and enhance the fertility of poor soil could contribute to achieving sustainable development. An indirect use of MSS in ditches alongside Pennisetum hybridum plants was studied in field plots for 30 months and the contents of heavy metals and macronutrients were monitored in soil, sludge and plant samples. We found that the yield of P. hybridum was significantly increased by 2.39 to 2.80 times and the treated plants had higher N content compared with no sludge. In addition, the organic matter (OM) and N contents in the planted soil increased significantly compared with the initial soil. The OM content in the planted soil of the MSS treatment was 2.9 to 5.2 times higher than that with no sludge, and N increased by 2.0 to 3.8 times. However, MSS had no significant effect on the N, P and K contents in the soil at the bottom of the MSS ditch, and the content of heavy metals (Cd, Pb, Cu and Zn) were also within the safe range. Moreover, the moisture content and phytotoxicity of MSS after this indirect use were reduced and the heavy metal contents changed little, which is favorable to the further disposal of recovered MSS. Therefore, this indirect use of MSS is beneficial to agricultural production, soil quality and environmental sustainability.     

Uncovering the evolution of tin use in the United States and its implications

• US tin use decreases as the GDP value added by manufacturing sector increases. • Global and China’s tin use increases as the GDP added by manufacturing increases. • A sigmoid curve can fit the US tin use data well. • US tin use patterns is not due to the finite tin reserves or resources. • Policies, substitutions, etc. play key roles in the changing tin use patterns. Tin is of key importance to daily life and national security; it is considered an essential industrial metal. The United States (US) is the world’s largest economy and consumer of natural resources. Therefore, the analysis of historical tin use in the US is helpful for understanding future tin use trends in the world as a whole and in developing countries. Time series analysis, regression analysis with GDP or GDP/capita, and historical data fitted with logistic and Gompertz models are employed in this study. Historical tin use in the US shows three stages—increase-constant-decrease, as GDP per capita has increased. Tin use in the US is negatively correlated with the GDP value added by the manufacturing sector, while the use of tin worldwide and in China continues to increase along with the GDP value added by the manufacturing sector. Although a sigmoid curve can fit the US tin use data well, that use is not directly related to the limited tin reserves or resources. Rather, policies, economic restructuring, substitutions, new end-use markets, etc. have played key roles in the changing tin use patterns. This work contributes to understanding future tin use at both the global and national levels: tin use will continue to increase with GDP at the global level, but use patterns of tin at the national level can be changed through human intervention.     

兰西城市群县域土地利用效率时空差异研究

在考虑绿色发展内涵的基础上,运用SBM-Undesirable模型测度了2006—2018年兰西城市群县域土地利用效率值,并采用泰尔指数、空间自相关方法揭示其时空差异特征。结果表明:①2006—2018年兰西城市群县域土地利用效率稳步提升,区域差异先扩大后缩小。②兰西城市群县域土地利用效率呈现“中部高—外围低、西高东低、南北分异”的空间差异规律,两省域呈现“西—东”的递减规律。③兰西城市群县域土地利用效率空间集聚特征显著,局部范畴上高—高集聚区在中部地区较为典型,低—低集聚区出现由东南向西南的扩散现象。     

多风险源驱动下的土地生态风险评价——以江苏省射阳县为例

我国正处于快速城镇化阶段,土地利用格局变化改变了土地生态系统的结构和功能,造成土地生态风险的进一步加剧和风险来源的多样化。论文根据风险来源、风险受体和风险效应的作用关系,提出了基于多源风险的发生概率、风险受体的抵御和自我恢复能力及其暴露于风险环境下可能发生的损失的土地生态风险表征方法。选取适当的指标计算得出风险来源指数、自然系统损失度和恢复力、社会系统损失度和恢复力、自然风险指数、社会风险指数和综合风险指数。论文以江苏射阳县为例开展实证研究,结果表明：射阳县城所在地社会风险指数最高,东部沿海丹顶鹤自然保护区自然风险指数最高,需要加强对这些地区的限制建设和生态保护。县城周边以及中东部大片区域综合风险指数较低,可以进行进一步的城镇发展和土地开发利用。论文提出的土地生态风险评价方法可为土地利用的风险评估提供方法参考,研究结论可为射阳新型城镇化发展与生态环境保护提供决策参考。     

秦岭山地丹江流域土地利用变化的土壤侵蚀效应评价

论文采用ArcGIS 10.0及InVEST土壤保持模型,分析2000—2010年秦岭山地丹江流域土地利用类型变化特征,模拟流域不同时期不同土地利用类型土壤侵蚀及土壤保持量的变化规律,并着重探讨土地利用类型方式转变对流域土壤侵蚀的影响。结果表明：1）2000—2010年间,流域裸地大比例减少90.18%（831.06 hm²）,主要转移至水域,耕地大面积减少5 197.24 hm²（4.11%）,主要流向灌丛和城镇;坡耕地还林还草初见成效,湿地的保护与恢复成效显著。2）2000—2010年间,流域土壤侵蚀状况较为严重,整体处于中度侵蚀至强度侵蚀级别,但10 a间侵蚀状况有减缓趋势;在该研究时段内,耕地大面积转为灌丛是该流域由土地利用类型变化引起的土壤侵蚀减缓的主要原因;另外,耕地转为林地以及裸地面积的减少也起到了减轻土壤侵蚀的作用;以自然生态系统为主的林地、灌丛及草地转为耕地时,土壤侵蚀强度则会明显增加。3）生态系统土壤保持功能受多方因素共同影响;2000、2010年研究区实际土壤保持量分别为5.35×10⁸、5.47×10⁸ t;占全区面积一半以上的林地和灌丛单位面积土壤保持量较为稳定,全区土壤保持功能有所提高。保证一定面积的林地、在人工干预下合理安排坡耕地还林还草区域的空间分布是秦岭山地丹江流域减少土壤侵蚀的必要措施,同时应注重对可利用耕地的保护。     

晋北地区1986~2010年土地利用/覆被变化的驱动力

为定量研究不同时期区域土地利用变化的不同驱动特征,以地处农牧交错带的晋北地区为例,采用非监督分类与人工目视解译相结合的方法,获取研究区1986~2010年间的土地利用/覆被变化（LUCC）状况,采用典范对应分析（CCA）方法定量分析了不同阶段LUCC的自然与人为驱动特征.结果表明：1）CCA在分析长时期序列LUCC驱动力上有较好的解释力,CCA排序可以很好地提取LUCC与自然、人为因素之间的关系,反映不同时期土地利用变化受到的驱动作用;2）研究区土地利用类型以耕地、草地、林地为主,土地利用变化类型以耕地与草地、林地与草地之间的互相转移为主;3）从不同时期LUCC的驱动因子来看,人口密度、人均国内生产总值（GDP）、降水、坡度和高程是研究区各时期LUCC的主要驱动力;研究区在1986~1995年间LUCC的主要驱动力来自于人口增长和经济发展的需求,而随着时间的推进,坡度和高程等地形因素的驱动作用也愈来愈重要;4）从不同地类变化的驱动因子来看,耕地、草地等地类向居民用地、工矿用地转移的主要驱动力是人口和经济的发展;而林地,草地等土地覆被类型之间的转移主要受到坡度,高程和降水等自然因素的驱动.     

基于LUCC的艾比湖区域生态风险评价及预测研究

以新疆内陆艾比湖流域典型区域为研究区,基于RS和GIS技术分析1998、2013年土地利用变化,尝试用CA-Markov模型预测2028年土地利用/覆盖变化.借助Fragstats3.4软件,基于土地利用/覆盖变化构建景观生态风险评价模型,分析1998~2028年景观生态风险的时空分异特征.结果表明：（1）1998~2013年,研究区土地类型面积变化明显.耕地面积增加量最大,增加的面积为152139hm²,而未利用地面积减少量最大,减少的面积为67605hm².2013~2028年,耕地和裸露的河床及盐渍地的面积增加明显,增加的面积分别为30730hm²,12427hm²,而未利用地和水体的面积分别从954376hm²和44889hm²,减至921079hm²和37157hm².（2）1998~2028年,研究区生态风险等级空间分布差异明显.高生态风险区面积变化较为显著,其面积分别约占总面积的36.6%,7.3%,23.7%.1998~2028年,全局Moran's Ⅰ值分别为0.436962,0.442202,0.506622,表现为一定程度的正相关.（3）1998~2028年,耕地分布在低,较低生态风险的比重上升,所占百分比分别为58.46%,78.58%,79.9%.林、草地类型的各生态风险等级的所占的比重的波动较大.     

Approaches and terminology for causal analysis in land systems science

Research into land and social-ecological systems science could benefit from improved clarity in the terminology used for causal analysis and a structured way to make causal inferences. Here I identify two aspects of causality, i.e. causal effects and causal mechanisms, and discuss explanation in historical sciences. I then propose definitions for the major terms used for causal relations, including driver, (spatial) determinant, location and contextual factor, proximate and underlying factors. Finally, I discuss the contribution of various operational approaches, including time series and counterfactual approaches for assessing causal effects and process-tracing approaches for establishing causal mechanisms. Having a coherent concept of causality, agreeing on a precise vocabulary and harnessing our tools with the clear purpose of establishing both causal effects and causal mechanisms should strengthen causal explanations for single cases, for drawing policy-relevant lessons and for theoretical development in relation to land and, more broadly, social-ecological systems processes.     

Exploration of spatial morphometry and socioeconomic variables in modelling urban land use change

The incorporation of land use (LU) data with socioeconomic data is a main issue in modelling. This is as a result of difference in data model and scale. This study proposed and tested the change–pattern approach, which allows the incorporation of these data sets in modelling LU change. Focusing on LU dynamics for a selected part of the Thames Gateway within the City of London, the approach tested two different methods of input selection for the modelling operations. Variables selected from these two methods serve as inputs into several neural networks tested in order to identify the direction of change for each of the LU types within the study area. The result shows that direction of LU change across the study area could be identified when spatial morphology of the area and socioeconomic variables are considered. Some classes of change could be identified fairly accurately using landscape metrics indicating level of fragmentation, extent of LU patches, shape complexity of LU patches in combination with some socioeconomic variables.