131 resultados para climate trend
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城市增温的原因包括全球变暖和城市热岛效应两个方面,二者对城市环境、社会经济和市民健康均有相当程度的影响。本文的研究目的是:(1)通过比较处于不同气候带上同样规模城市的气温变化趋势和速率差异,探讨地理位置对城市增温现象的影响;(2)通过分析近期人类活动和城市发展规模与城市增温现象的相关性,搞清楚城市化发展过程中显著影响热岛效应的因素。了解城市增温的地理分异规律及其受城市化发展的影响,对全面认识城市增温现象、积极寻求应对城市增温所造成的环境危害的策略具有重要的科学和实践意义。 本文按照经纬度在全国范围内选取6个特大城市:济南、西安、兰州、广州、上海和北京为研究对象,按城市所处地理位置分为代表水分梯度的同纬度经向分布城市,近海到内陆依次为济南、西安和兰州,以及代表温度梯度的纬向分布城市,低纬度到高纬度依次为广州、上海和北京,借助统计学方法,对各城市分别进行了年均气温比较分析,并对近期人类活动对不同城市增温效应的影响进行了分析。结果表明: 1.各城市气温均呈上升趋势,其中年均最低气温上升幅度最大,年均气温上升幅度次之,年均最高温度上升幅度最小;温度普遍升高的前提下高纬度地区温度升幅较大,内陆地区增温比近海地区大,即城市增温幅度与水分梯度和温度梯度呈负相关关系;不同城市在不同年代冷暖变化的强度和峰谷相位不尽一致,北京、西安和广州从上世纪50年代到70年代气温整体趋势变冷,其他城市缓慢升温,进入80年代后6个城市均进入加速增温阶段。 2.城市热岛效应对最低气温影响最明显,即城市最低气温与参照站差值增长趋势最为显著,其次为年均温,市区最高气温与参照站差值增长趋势最缓慢;自1978年改革开放以来,6个城市年均最低气温和年均温城乡差值均达到极显著水平,兰州最高,达0.69℃/lOa和0.49℃/lOa;从近海到内陆随着年降水量减少,3个城市(依次为济南、西安和兰州)热岛效应依次增加,从高纬度到底纬度随着温度升高(北京、上海和广州),城市热岛效应有减小趋势。 3.不同城市增温均表现出与人口(包括市辖区年末总人口、市辖区人口密度)、市辖区地区生产总值、年末实有道路面积、建成区面积和第二产业占GDP比重等代表城市发展因素的指标呈显著正相关,与绿地有关的因素,包括园林绿地面积和年末耕地面积呈显著负相关,而同样的因素对同一个城市不同气候参数的影响也不相同,最低气温对增温因子的敏感度高于其他气温参数,而对降低增温效应因子的敏感度小于其他气候参数,同样的因素对不同城市气候参数也有不同效应。 本项研究的结果证实了城市增温是一个比较复杂的过程,其中即反映了全球气候变化的大背景,也受到了影响水热环境的地理因素的制约,同时又与城市化发展的进程密切相关。
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CpG islands (CGIs) are often considered as gene markers, but the number of CGIs varies among mammalian genomes that have similar numbers of genes. In this study, we investigated the distribution of CGIs in the promoter regions of 3,197 human-mouse ortholo
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Previous studies have proposed that selection has been involved in the differentiation of human mitochondrial DNA (mtDNA) and climate was the main driving force. This viewpoint, however, gets no support from the subsequent studies and remains controversia
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In Asia, especially in China, our knowledge of the distribution of testate amoebae is still limited. In this paper, the geographical distribution of testate amoebae in Tibetan Plateau and northwestern Yunnan Plateau, southwest China and their relationships with the climatic factors have been studied. We found testate amoebae shifted in the most dominant species and increased in species (or genus) richness from northwest to southeast. Further, the linear regression analyses revealed that both species richness and genus richness have higher positive correlations with the mean temperature of the warmest month and annual mean precipitation as contrasted with the mean altitude, which showed weak negative correlation. This indicates that the temperature and precipitation are more significant influences on the richness than the altitude. The cluster analysis based on the community structure, defined by Sorenson's coefficient matrix, suggested four groups from the 10 physiographical regions. This geographical distribution pattern was also closely related with the climatic regionalization. The present climatic regionalization pattern of the study area originated from the uplift of Tibetan Plateau and mainly occurred in or after the late Pleistocene. Therefore, the geographical distribution of testate amoebae in our study area may have experienced complicated and drastic changes corresponding to the variation of the climate caused by the geological events.