北京地区冬夏季持续性雾-霾发生的环境气象条件对比分析

在北京地区,除冬季供暖期外盛夏也是雾-霾天气的高发季节,与我国南方不同.使用微波辐射仪、风廓线和常规气象探测资料、NCEP再分析资料以及大气成分观测结果,通过对比分析揭示了冬、夏季持续6 d的2个雾-霾过程形成和维持机制的异同.冬季雾-霾过程出现在高空西北气流、低层多短波活动的背景下,其形成和维持的主要机制是边界层内始终有逆温层、地面弱风场、底层湿度逐渐增大.逆温层昼高夜低、湿度昼小夜大是影响PM2.5质量浓度和能见度日变化的重要环境因子.在雾-霾天气持续期间地面弱风场能够维持主要源于冷空气势力弱、常不能影响到地面.此外,入夜后地面迅速辐射降温、边界层上层有暖平流以及空气过山后下沉增温在逆温层的形成中起了关键作用.然而,对于夏季持续性雾-霾天气,气溶胶区域输送、环境大气保持对流性稳定、空气的高饱和度是其发生的重要条件.在副热带高压长时间控制下对流层低层盛行偏南风,北京的PM2.5质量浓度随着偏南风风速增大升高.对流层底层系统性偏南风与北京附近的山谷风共同构成了从北京以南气溶胶累积地向北输送的机制.夏季雾-霾过程低层没有逆温,但是北京上空一直维持超过200 J·kG-1的对流抑制能量,它同样限制了污染物的垂直扩散.夏季自由对流高度也存在昼夜变化,其对PM2.5浓度和能见度的作用与逆温层高度升降相同.因此,冬、夏个例分别代表了2种不同类型的持续性雾-霾过程,导致差异的根本原因在于大气环流型.     

普兰店市土地利用景观格局及其生态环境效应研究

基于研究区2010年的土地利用图形数据,选用生态意义较明确的景观特征指数,定量分析了普兰店市景观空间格局及其生态环境效应.结果表明：（1）普兰店市旱地景观面积最大、分布最广,其面积比例为47.1％,林地景观的面积比例也较大,草地和沼泽地的面积比例较小.平均斑块面积0.82 km2/个,景观多样性指数为1.35,优势度指数为0.73,均匀度指数为0.62;（2）大部分旱地景观分布在普兰店市南部,林地景观大多分布在北方.城乡用地、水田则相对集中分布在南部沿海地区;（3）普兰店市的生态环境指数为0.40,生态环境质量总体一般.     

澳大利亚新南威尔士州《污染土地管理法》及其实施效果

澳大利亚新南威尔士州1997年出台的《污染土地管理法》核心内容包括污染土地责任人的界定、初步调查令、重大污染土地的认定、治理令、自愿治理方案、继续治理令,以及污染土地治理费用的偿还,即环保署支付费用的偿还、公共权力机构执行命令时支付费用的偿还、当事人之间费用的偿还和统一基金拨款的偿还。《污染土地管理法》实施以来,效果较好。     

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.     

The application of generalized linear mixed models to multi-level sampling for insect population monitoring

Iwao's quadratic regression or Taylor's Power Law (TPL) are commonly used to model the variance as a function of the mean for sample counts of insect populations which exhibit spatial aggregation. The modeled variance and distribution of the mean are typically used in pest management programs to decide if the population is above the action threshold in any management unit (MU) (e.g., orchard, forest compartment). For nested or multi-level sampling the usual two-stage modeling procedure first obtains the sample variance for each MU and sampling level using ANOVA and then fits a regression of variance on the mean for each level using either Iwao or TPL variance models. Here this approach is compared to the single-stage procedure of fitting a generalized linear mixed model (GLMM) directly to the count data with both approaches demonstrated using 2-level sampling. GLMMs and additive GLMMs (AGLMMs) with conditional Poisson variance function as well as the extension to the negative binomial are described. Generalization to more than two sampling levels is outlined. Formulae for calculating optimal relative sample sizes (ORSS) and the operating characteristic curve for the control decision are given for each model. The ORSS are independent of the mean in the case of the AGLMMs. The application described is estimation of the variance of the mean number of leaves per shoot occupied by immature stages of a defoliator of eucalypts, the Tasmanian Eucalyptus leaf beetle, based on a sample of trees within plots from each forest compartment. Historical population monitoring data were fitted using the above approaches.     

Temporal Trends in the Spatial Distribution of Impervious Cover Relative to Stream Location

Use of impervious cover is transitioning from an indicator of surface water condition to one that also guides and informs watershed planning and management, including Clean Water Act (33 U.S.C. §1251 et seq.) reporting. Whether it is for understanding surface water condition or planning and management, impervious cover is most commonly expressed as summary measurement (e.g., percentage watershed in impervious cover). We use the National Land Cover Database to estimate impervious cover in the vicinity of surface waters for three time periods (2001, 2006, 2011). We also compare impervious cover in the vicinity of surface waters to watershed summary estimates of impervious cover for classifying the spatial pattern of impervious cover. Between 2001 and 2011, surface water shorelines (streams and water bodies) in the vicinity of impervious cover increased nearly 10,000 km. Across all time periods, approximately 27% of the watersheds in the continental United States had proximally distributed impervious cover, i.e., the percentage of impervious cover in the vicinity of surface waters was higher than its watershed summary expression. We discuss how impervious cover spatial pattern can be used to inform watershed planning and management, including reporting under the Clean Water Act.     

长江中游城市群经济与生态成本空间演化模式分析

综合运用变异系数、泰尔指数及ESDA-GIS多种方法,在充分考虑传统经济影响因子的基础上,尝试引入经济发展生态成本指标,分析长江中游城市群经济综合发展水平与生态成本的空间匹配及演化模式。研究表明:经济综合发展水平高低聚集特征明显,空间极化现象是导致区域经济发展不平衡的主要原因;生态成本空间集聚现象以高高集聚和低低集聚为主,且呈现出高高集聚减少、低低集聚增多的现象,生态成本消耗有所改善;2001~2012年长江中游城市群市域经济综合发展水平与生态成本表现出越来越显著的差异化空间格局,呈现出由低经济综合发展水平-低生态成本过渡到高经济综合发展水平-高生态成本,进而发展到高经济综合发展水平-低生态成本的空间匹配模式的阶段性特征;高经济综合发展水平-高生态成本的经济发展模式仍在大部分城市中存在,经济与生态空间协调发展的潜力仍然很大。