我国人为源气态活性氮排放时空变动特征分析

随着粮食和能源需求的增加,农业生产和化石燃料燃烧带来的活性氮污染越来越成为影响我国环境和人民健康的重要因素之一.通过对2000、2005及2010年全国31个省(直辖市、自治区)的人为源气态活性氮排放总量、单位GDP及单位人均排放量的Moran's I指数和GetisOrd G*i指数进行空间分析,探讨了气态活性氮排放及其强度的时空变动特征.结果表明,全国的气态活性氮排放及其强度呈现明显的空间集聚特征.在空间分布特征上,全国活性氮排放表现为高值集聚分布,其热点主要位于华北平原和长江中下游平原地区;排放强度方面则为高值聚集和低值聚集兼备.通过对3个年份的冷热点比较分析发现,排放强度的冷点地区变化较小,热点地区变化较大.其中,单位GDP排放强度方面,冷点地区处于东南沿海地区,热点地区则由西北地区缩小到四川、甘肃两省;而单位人口排放强度方面,冷点地区范围有所缩减,热点地区则由分散转变为集中分布于京津冀及周边的山西、内蒙古和辽宁等.分析人为源气态活性氮时空分布格局和特征及其形成原因,可为评估和减缓气态活性氮排放对生态环境的影响提供科学基础.     

基于本地污染源调查的杭州市大气污染物排放清单研究

基于实地调查数据并辅以统计数据,采用物料衡算法和排放因子法,估算了杭州市2015年大气污染物排放清单,并选取经纬度坐标、路网、航道、土地类型和人口等数据作为权重因子,研究了该地区各类排放源污染物排放空间分布特征.结果表明,杭州市2015年SO_2、NO_x、CO、VOCs、PM_(10)、PM_(2.5)和NH_3年排放总量分别为22.20×10~3、108.17×10~3、192.10×10~3、134.94×10~3、78.12×10~3、27.65×10~3和59.75×10~3t.工业源是杭州市SO_2排放的主要来源,移动源对NO_x和CO的排放贡献最为显著,扬尘源是杭州市PM_(10)和PM_(2.5)排放的最主要来源,其次为工业源;VOCs排放的主要来源依次为工业源、天然源和移动源;NH_3排放主要来自农业源.从空间分布来看,排放主要集中在中心城区及其周边的萧山、下沙、大江东、余杭和富阳等工业企业相对密集的区域.本研究建立的排放清单在污染源覆盖范围和排放因子方面仍然存在一定的不确定性,建议在后续研究中重点开展低、小、散企业及本地化排放因子调查研究工作,进一步提升大气污染物排放清单的准确度.     

北京2015年大气细颗粒物的空间分布特征及变化规律

近年来,随着雾霾事件的频发,人们逐渐提高了对雾霾的关注度,PM_2.5作为其首要污染物对大气能见度及人体健康造成了严重影响.因此,本文利用2015年北京12个环境监测站点的PM_2.5、PM₁₀、NO₂、SO₂、CO和O₃的浓度数据和气象数据,综合研究了北京2015年大气细颗粒物的空间变化特征及分布规律.同时,利用空间差异率（COD）统计方法评估了不同地区细颗粒物浓度的差异程度,并结合2015年2次特殊事件（春节和阅兵）,对大气污染特征及其与排放源控制的关系进行了深入对比分析.结果发现,重污染天气集中发生在秋冬季,且污染程度高、持续时间长.城区PM_2.5浓度比郊区高约12 μg·m^-3.东城区与对照区差异最大,COD值为0.24;东城区与西城区差异最小,COD值为0.05.春、夏、秋季颗粒物PM_2.5、PM₁₀浓度日变化较为平稳,在中午有所升高,冬季颗粒物质量浓度明显呈现出夜间高于日间的污染模式.近3年PM_2.5与PM₁₀保持显著的相关性,但PM_2.5/PM₁₀比值呈降低趋势.阅兵期间采取的空气质量管控措施和气象要素共同作用导致PM_2.5浓度下降约72%,市中心的首要污染物为NO₂,郊区首要污染物为O₃和PM₁₀.春节期间烟花燃放对PM_2.5的瞬时贡献量很大,对比春节假期和非假期2个阶段的大气污染特征发现,人口和机动车减少及餐馆暂停营业并没有使北京局地空气质量得到明显改善.该研究结果提示在进行PM_2.5控制的同时也要对O₃浓度有所关注,同时也进一步支撑了北京空气质量改善需要京津冀协同控制这一重要结论.     

多孔介质中污染物运移空间变异最优估计

论述了空间变异性分析的原理和方法,着重阐述不同估值方法在多孔介质污染物运移空间变异分析中的应用.实践表明,地质统计学方法能够弥补经典概率统计方法在评价区域化变量空间变异性方面的不足,从而对多孔介质中污染物运移的空间变异性做出更切合实际的分析与评价.     

二维相关地震动作用下结构的随机反应

假定两水平方向的地震动为平稳随机过程,基于地震动主轴模型的假设,讨论了二维地震动分量的空间相关性,在单分量平稳模型的基础上给出了二维水平地震动随机模型的相关函数矩阵。该相关函数模型可以为二维地震下结构随机反应分析提供地震动输入。利用状态空间方法和随机振动理论建立了线性多自由度体系在二维地震激励下的随机反应分析方法,导出了结构的位移反应相关函数和速度反应相关函数,可在时域内直接计算结构的二维随机地震反应的统计特征。最后,通过一个二层框架结构计算实例,说明了这种方法的运用,讨论了地震动强度以及地震动主轴方位角对结构反应的影响。该方法可以为结构抗震可靠性评估提供基础。     

郑州园林绿化树木低温寒冻害空间分布特征研究

绿化树木是园林景观的主体部分,同样也是园林建设中的目标植物.然而,绿化植物容易受到低温寒冻害的影响,进而影响园林布局美观.基于此,该文提出郑州园林绿化树木低温寒冻害空间分布特征研究方法,统计郑州园林绿化树木类型,对各样本按照乔木层与灌木层进行分类整理,将频度、显著度、密度等作为计算树木多样性的指标,分析比较结果,根据获...     

西藏冰湖溃决主要特征

西藏是我国冰湖分布最多的地区。冰湖常发生溃决,其中最典型是终碛冰湖的溃决。冰湖溃决所导致的泥石流,具有突发性强、频率低、洪峰高、流量大、流量过程暴涨暴落和破坏力强及灾害波及范围广等特点。从20世纪30年代中期至今,已查明西藏15个冰川终碛湖至少发生过18次溃决。通过对这些溃决事件的分析,探讨了终碛湖溃决的时空分布特征,发现该事件的发生与气候变化,特别是与气候异常年份有显著的对应关系。结合近年冰湖分布和变化资料,讨论了未来的发展趋势。具体分析了溃决洪水过程和冰湖溃决洪水泥石流演变过程。     

Characteristics and driving factors of marsh changes in Zhalong wetland of China

Zhalong National Nature Reserve in the northeast of China is a large wetland and a habitat of hundreds species of fauna and flora. The rare red-crowned crane is one kind of endangered birds in it. Recently, Zhalong wetland is shrinking and it encounters many problems including occasional fires, bad water quality, human activities, etc. In order to find out a proper way to protect and restore the wetland, this study, using a geographic information system, the global positioning system and remote sensing techniques, analyses the spatial characteristics of the changes in marsh landscape pattern and examines the driving factors for these changes. Data sources include 8 Landsat Thematic Mapper satellite images of Zhalong area in the period of 1986–2002 and the investigation information on site. Based on the analysis of changes of marsh area and annual precipitation during the 16 years, it is found that there is a close correlation between annual precipitation and marsh area. It means that climate is one of driving factors of marsh pattern changes. To understand influences of other kinds of land uses on marsh spatial distribution in Zhalong wetland, this paper analyses the relationship between marsh and different kinds of land uses, such as water surface, residential area, farmland, salina land and grass land, respectively. According to the patch analysis theory, a fragmental index and a fractal dimension of the marsh are calculated with perimeter-area method. The results indicate that the marsh pattern is affected by human activities significantly. In addition, the location alteration of marsh centroid point over the 16 years is studied. The movement trace of marsh centroid point is concerned with different hydrological situation in different areas of the wetland. In summary the characteristics of the marsh landscape pattern evolution during the 16 years are affected by multiple driving factors. The main driving factors are climate, human activities, distribution of other kinds of land uses and hydrological situation in different areas.     

Using Spatial Pattern to Quantify Relationship Between Samples, Surroundings, and Populations

The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas. As a consequence, approaches for investigating how well sample data represent larger areas (i.e., the sample neighbourhood and the population) are desired. Presented in this communication is a quantitative approach for assessing the nature of sampled areas in relation to surrounding areas and the overall population of interest. Classified Landsat data is converted to forest/non-forest categories to provide a consistent and uniform data set over a 130,000 km² study region in central British Columbia, Canada. From this larger study area 322 2 × 2 km photo plots on a 20 × 20 km systematic grid are populated with composition and configuration information for comparison to non-sampled areas. Results indicate that typically, within the study area, the spatial pattern of forest within a photo plot is representative of the forest patterns found within primary and secondary neighbourhoods and over the entire population of the study. These methods have implications for understanding the nature of data used in monitoring programs worldwide. The ability to audit photo and field plot information promotes an increased understanding of the results developed from sampling and provides tools identifying locations of possible bias.     

Suspended Particulate Matter Distribution in Rural-Industrial Satna and in Urban-Industrial South Delhi

An air quality sampling program was designed and implemented to collect the baseline concentrations of respirable suspended particulates (RSP = PM₁₀), non-respirable suspended particulates (NRSP) and fine suspended particulates (FSP = PM_2.5). Over a three-week period, a 24-h average concentrations were calculated from the samples collected at an industrial site in Southern Delhi and compared to datasets collected in Satna by Envirotech Limited, Okhla, Delhi in order to establish the characteristic difference in emission patterns. PM_2.5, PM₁₀, and total suspended particulates (TSP) concentrations at Satna were 20.5 ± 6.0, 102.1 ± 41.1, and 387.6 ± 222.4 μg m⁻³ and at Delhi were 126.7 ± 28.6, 268.6 ± 39.1, and 687.7 ± 117.4 μg m⁻³. Values at Delhi were well above the standard limit for 24-h PM_2.5 United States National Ambient Air Quality Standards (USNAAQS; 65 μg m⁻³), while values at Satna were under the standard limit. Results were compared with various worldwide studies. These comparisons suggest an immediate need for the promulgation of new PM_2.5 standards. The position of PM₁₀ in Delhi is drastic and needs an immediate attention. PM₁₀ levels at Delhi were also well above the standard limit for 24-h PM₁₀ National Ambient Air Quality Standards (NAAQS; 150 μg m⁻³), while levels at Satna remained under the standard limit. PM_2.5/PM₁₀ values were also calculated to determine PM_2.5 contribution. At Satna, PM_2.5 contribution to PM₁₀ was only 20% compared to 47% in Delhi. TSP values at Delhi were well above, while TSP values at Satna were under, the standard limit for 24-h TSP NAAQS (500 μg m⁻³). At Satna, the PM₁₀ contribution to TSP was only 26% compared to 39% in Delhi. The correlation between PM₁₀, PM_2.5, and TSP were also calculated in order to gain an insight to their sources. Both in Satna and in Delhi, none of the sources was dominant a varied pattern of emissions was obtained, showing the presence of heterogeneous emission density and that nonrespirable suspended particulate (NRSP) formed the greatest part of the particulate load.