上海城市土地利用转变类型及其空间关联分析

基于多时段TM遥感影像资料,通过地图代数和空间关联分析、相邻度分析等空间分析手段的综合应用,论文对上海地区近10年来城市土地利用变化类型、形成过程和相关空间机制进行了研究,结果表明:①近10年来上海城市土地利用扩展经历了“单核扩展”模式为主(1987～1990年)到“单核扩展”、“多核扩展”与“点-轴”扩展模式并存(1990～1995年),直至“多核扩展”及“点-轴”等复杂扩展模式为主(1995～2000年)的变化过程,导致上海中心城区和郊县城镇规模急剧扩张,交通干道沿线区域城市化过程明显增强,并形成整个上海地区城市化网络结构;②面积贡献率较大、转变频率较低的城市土地利用变化类型,与其他类型之间具有较高的关联度和空间亲和性,构成中心城区或卫星城、郊区城镇的扩展核心;③面积贡献率较低的类型,其转变频率较高,分布零散,构成扩展核的外围部分,这些城市周边地区存在一定程度紊乱、不合理的土地利用、开发活动;④城市化过程的内在空间机制与土地利用类型的面积和转变频率有密切关系。在更精细的尺度上依据转变频率划分的类别在一定程度上能更好体现城市土地利用变化类型的空间分异,及其作为宏观驱动机制的社会经济因子所具有的空间差异性。     

Multivariate Analysis of Interactions Between Phytoplankton Biomass and Environmental Variables in Taihu Lake, China

Phytoplankton variation in large shallow eutrophic lakes is characterized by high spatial and temporal heterogenity. Understanding the pattern of phytoplankton variation and the relationships between it and environmental variables can contribute to eutrophic lakes management. In this study Taihu Lake, one of the largest eutrophic fresh water lake in China, was taken as study area. The water body of Taihu Lake was divided into five regions viz. Wuli bay (WB), Meilian Bay (MB), West Taihu Lake (WTL), Main Body of Taihu Lake (MBTL) and East Taihu Lake (ETL). Concentrations of chlorophyll-a and the related environmental variables were determined in each region in the period 2000–2003. Factor analysis and multivariate analysis were applied to evaluate the interactions between phytoplankton variation and environmental variables. Results showed that the highest average concentrations of TN, TP and Chl-a were observed in WB, followed in a descending order by MB and WTL, and the lowest concentrations of TN, TP and Chl-a were observed in MBTL and ETL. Chl-a and TP concentrations in most regions (except ETL) declined during the study period. It suggested that to some extent the lake was recovering from eutrophication. However, persistent ascending of TN and NH₄–N in all five regions indicated the deteriorating of water quality in the study period. Results of multivariate showed that the relationships between phytoplankton biomass and environmental variables varied among regions. TP illustrated itself a controlling role on phytoplankton in WB, MB, WTL and MBTL according to the significant positive relations to phytoplankton biomass in these regions. Nitrogen could be identified as a limiting factor to phytoplankton biomass in ETL in view of the positive correlations between TN and phytoplankton and between NH₄–N and phytoplankton. Spatial variation of interactions between phytoplankton and environmental parameters suggested proper eutrophication control measures were needed to restore ecological system in each region of Taihu Lake.     

长江上游重庆段近20 a降水时空变化特征

全球气候变暖背景下,中国的降水分布日趋复杂,气象灾害造成的损失巨大。为满足防灾减灾与水资源管理的数据支撑需求,基于长江上游重庆段近20 a降水数据,利用线性回归、M-K非参数检验等方法,探究降水的时间变化趋势与空间分异特征。结果表明：近20 a重庆市年降水量在875.6—1348.2 mm。时间上,春、秋两季呈明显增多的趋势,其中3月最显著。降水在14 a时间尺度的周期过程线中波动最强,是第一主周期。空间分布上,呈西低东高的特点,在渝西和长江河谷地区降水相对偏少,主要受重庆地形的影响;渝西和渝东北地区秋季降水显著增多。研究结果可为长江流域旱涝灾害防治、水资源开发利用及经济社会可持续发展提供数据支撑,同时可为植被修复与生态廊道保障提供参考。     

Changes in the Population Phenetic Structure of the Lacebug Dictyla humuli (Fabr.) (Heteroptera,Tingidae) in the Usman' Forest (Voronezh Oblast) in 1999–2001

Russian Journal of Ecology -     

超塑性材料轴对称镦粗时的变形力计算

本文探讨了变分理论在超塑性成形方面的应用,建立了适合超塑性材料的变分理论的基本方程,并比超塑性材料轴对称镦粗为例验证了这些方程的正确性。     

北京PM2.5浓度的变化特征及其与PM10、TSP的关系

在连续2年进行累积1周同步采样的基础上,对北京市城区和居住区2个采样点环境空气中PM2.5的浓度及其时间变化特征进行了分析.PM2.5周平均浓度的变化范围为37~346靏/m3,年均浓度接近或超过PM10的二级年均标准.PM2.5浓度具有明显的季节变化特征,即冬季最高,夏季最低.2个采样点PM2.5浓度的周变化与季节变化均相似.PM2.5与PM10、TSP的比值均在冬季最高,春季最低,反映采暖燃烧源对细颗粒物的贡献较大,而沙尘天气对粗颗粒物的贡献较大;其年均值分别为55%和29%.     

广州市1990-2005年植被覆盖度的时空变化特征

植被是生态系统的最重要组成部分,以植被覆盖度为指标研究区域植被的时空动态特征,是生态系统健康评价的前提和必要基础。以1990、1995、2000和2005年4个时相的TM遥感影像为数据源,对其进行大气辐射校正以及进行空间图像运算,生成广州市不同时序的植被覆盖度图,以此分析广州市植被覆盖度的时空变化特征。结果表明,广州市过去15a植被覆盖度变化很大,从58.75%下降到46.06%,特别是中心城区和番禺区内的植被覆盖度变化更显著。植被覆盖度下降最多的时间段是1990—1995年,恢复阶段是2000—2005年间。但是,从空间分布上看,植被恢复的区域主要是从化市,市区仍然处于继续下降状态,值得各方面关注,因为市区是人口密度和经济高度集中的区域,也是生态元素最缺乏的区域。     

Changes in quality of groundwater with seasonal fluctuations: an example from Ghor Safi area, southern Dead Sea coastal aquifers, Jordan

Introduction The alluvial fan along the eastern and southern m argins of the D ead Sea (hereafter denoted D S) consists of fine to coarse-grained siliclastic sand and pebble-size gravel w ith thin intercalations of lam inated m arl of H olocene age (Pow e…     

Vehicle emissions of primary air pollutants from 2009 to 2019 and projection for the 14th Five-Year Plan period in Beijing, China

Over the past decade, the emission standards and fuel standards in Beijing have been upgraded twice, and the vehicle structure has been improved by accelerating the elimination of 2.95 million old vehicles. Through the formulation and implementation of these policies, the emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NO_x), and fine particulate matter (PM_2.5) in 2019 were 147.9, 25.3, 43.4, and 0.91 kton in Beijing, respectively. The emission factor method was adopted to better understand the emissions characteristics of primary air pollutants from combustion engine vehicles and to improve pollution control. In combination with the air quality improvement goals and the status of social and economic development during the 14th Five-Year Plan period in Beijing, different vehicle pollution control scenarios were established, and emissions reductions were projected. The results show that the emissions of four air pollutants (CO, VOCs, NO_x, and PM_2.5) from vehicles in Beijing decreased by an average of 68% in 2019, compared to their levels in 2009. The contribution of NO_x emissions from diesel vehicles increased from 35% in 2009 to 56% in 2019, which indicated that clean and energy-saving diesel vehicle fleets should be further improved. Electric vehicle adoption could be an important measure to reduce pollutant emissions. With the further upgrading of vehicle structure and the adoption of electric vehicles, it is expected that the total emissions of the four vehicle pollutants can be reduced by 20%-41% by the end of the 14th Five-Year Plan period.