生态环境学报
生態環境學報
생태배경학보
ECOLOGY AND ENVIRONMENT
2014年
9期
1425-1431
,共7页
城市化%区域气温变化%灰色关联度%南京
城市化%區域氣溫變化%灰色關聯度%南京
성시화%구역기온변화%회색관련도%남경
urbanization%regional temperature change%gray correlation degree%Nanjing
20世纪90年代以来中国进入城市化快速发展阶段,城市规模迅速扩张,这在一定程度上对大气热环境产生了影响,如产生了城市热岛效应。文章基于南京气象站点观测数据、南京市统计年鉴以及 landsat TM影像数据,选取人口密度、废气排放量、运营车辆、用电量、绿地覆盖面积、建成区面积6项指标构建城市化因子群,运用灰色关联度分析法对影响南京气温变化的因子群进行贡献度分析。首先,基于以往研究及南京市统计年鉴选取人口密度、废气排放量、运营车辆、用电量、绿地覆盖面积、建成区面积6项指标构建城市化因子群;其次,基于landsat TM影像数据利用监督分类方法提取建成区面积;最后,基于灰色关联度分析方法,定量计算出人口密度、废气排放量、运营车辆、用电量、绿地覆盖面积、建成区面积6项城市化因子分别对年均温、年最高温、年最低温、季均温、月均温以及不同时期温度均值的影响。研究发现,(1)1983─2011年期间,南京市气温呈明显递增趋势,20世纪90年代后期增温更为明显,1999─2007年年均温增长了1.50℃。(2)发现对于同一参考数列(年均温、月均温等)而言,其影响因子关联度整体排序是一致的:人口密度>建成区面积>废气排放量>运营车辆>用电量>绿地覆盖面积。(3)同一城市化因子对年均温变化、年最高温变化、年最低温变化的影响是不相同的。例如,人口密度对1983─2011年年均温变化影响最大,关联度达到了0.95;用电量、废气排放量和运营车辆对1983─2011年年最低温变化影响最大,其关联度分别为0.68、0.74、0.73。(4)同一城市化因子对不同月份气温变化的影响是不相同的,如人口密度与2月月均温之间的关联度最小,关联度为0.78;与3月月均温之间的关联度最大,关联度为0.93。(5)不同城市化因子随着时间的推移,对区域气温变化的影响也是不相同的。其中,人口密度、运营车辆以及建成区面积对气温变化的影响是立竿见影的效应;用电量和废气排放量对气温变化的影响是累加的效应;而城市绿地面积对气温的影响只是对温室气体吸收量多少的外在表现,一般绿地面积越多,吸收的温室气温也就越多,无附加影响。
20世紀90年代以來中國進入城市化快速髮展階段,城市規模迅速擴張,這在一定程度上對大氣熱環境產生瞭影響,如產生瞭城市熱島效應。文章基于南京氣象站點觀測數據、南京市統計年鑒以及 landsat TM影像數據,選取人口密度、廢氣排放量、運營車輛、用電量、綠地覆蓋麵積、建成區麵積6項指標構建城市化因子群,運用灰色關聯度分析法對影響南京氣溫變化的因子群進行貢獻度分析。首先,基于以往研究及南京市統計年鑒選取人口密度、廢氣排放量、運營車輛、用電量、綠地覆蓋麵積、建成區麵積6項指標構建城市化因子群;其次,基于landsat TM影像數據利用鑑督分類方法提取建成區麵積;最後,基于灰色關聯度分析方法,定量計算齣人口密度、廢氣排放量、運營車輛、用電量、綠地覆蓋麵積、建成區麵積6項城市化因子分彆對年均溫、年最高溫、年最低溫、季均溫、月均溫以及不同時期溫度均值的影響。研究髮現,(1)1983─2011年期間,南京市氣溫呈明顯遞增趨勢,20世紀90年代後期增溫更為明顯,1999─2007年年均溫增長瞭1.50℃。(2)髮現對于同一參攷數列(年均溫、月均溫等)而言,其影響因子關聯度整體排序是一緻的:人口密度>建成區麵積>廢氣排放量>運營車輛>用電量>綠地覆蓋麵積。(3)同一城市化因子對年均溫變化、年最高溫變化、年最低溫變化的影響是不相同的。例如,人口密度對1983─2011年年均溫變化影響最大,關聯度達到瞭0.95;用電量、廢氣排放量和運營車輛對1983─2011年年最低溫變化影響最大,其關聯度分彆為0.68、0.74、0.73。(4)同一城市化因子對不同月份氣溫變化的影響是不相同的,如人口密度與2月月均溫之間的關聯度最小,關聯度為0.78;與3月月均溫之間的關聯度最大,關聯度為0.93。(5)不同城市化因子隨著時間的推移,對區域氣溫變化的影響也是不相同的。其中,人口密度、運營車輛以及建成區麵積對氣溫變化的影響是立竿見影的效應;用電量和廢氣排放量對氣溫變化的影響是纍加的效應;而城市綠地麵積對氣溫的影響隻是對溫室氣體吸收量多少的外在錶現,一般綠地麵積越多,吸收的溫室氣溫也就越多,無附加影響。
20세기90년대이래중국진입성시화쾌속발전계단,성시규모신속확장,저재일정정도상대대기열배경산생료영향,여산생료성시열도효응。문장기우남경기상참점관측수거、남경시통계년감이급 landsat TM영상수거,선취인구밀도、폐기배방량、운영차량、용전량、록지복개면적、건성구면적6항지표구건성시화인자군,운용회색관련도분석법대영향남경기온변화적인자군진행공헌도분석。수선,기우이왕연구급남경시통계년감선취인구밀도、폐기배방량、운영차량、용전량、록지복개면적、건성구면적6항지표구건성시화인자군;기차,기우landsat TM영상수거이용감독분류방법제취건성구면적;최후,기우회색관련도분석방법,정량계산출인구밀도、폐기배방량、운영차량、용전량、록지복개면적、건성구면적6항성시화인자분별대년균온、년최고온、년최저온、계균온、월균온이급불동시기온도균치적영향。연구발현,(1)1983─2011년기간,남경시기온정명현체증추세,20세기90년대후기증온경위명현,1999─2007년년균온증장료1.50℃。(2)발현대우동일삼고수렬(년균온、월균온등)이언,기영향인자관련도정체배서시일치적:인구밀도>건성구면적>폐기배방량>운영차량>용전량>록지복개면적。(3)동일성시화인자대년균온변화、년최고온변화、년최저온변화적영향시불상동적。례여,인구밀도대1983─2011년년균온변화영향최대,관련도체도료0.95;용전량、폐기배방량화운영차량대1983─2011년년최저온변화영향최대,기관련도분별위0.68、0.74、0.73。(4)동일성시화인자대불동월빈기온변화적영향시불상동적,여인구밀도여2월월균온지간적관련도최소,관련도위0.78;여3월월균온지간적관련도최대,관련도위0.93。(5)불동성시화인자수착시간적추이,대구역기온변화적영향야시불상동적。기중,인구밀도、운영차량이급건성구면적대기온변화적영향시립간견영적효응;용전량화폐기배방량대기온변화적영향시루가적효응;이성시록지면적대기온적영향지시대온실기체흡수량다소적외재표현,일반록지면적월다,흡수적온실기온야취월다,무부가영향。
Due to the long period of rapid urban development since 1990’s, the atmospheric thermal environmental impact has been influenced to some extent in many Chinese cities, resulting in significant urban heat island effect. In this paper, potential indicators are proposed to identify major urbanization factors in Nanjing which may have significant influence on the regional temperature changes based on analyzing the Nanjing meteorological observation data, statistical yearbook of Nanjing city and the Landsat TM image data. The grey correlation degree analysis method is adopted to analyze the factor contributions on the influence of temperature variation. Firstly, based on the previous research and the statistical yearbook of Nanjing, six potential indicators including the population density, exhaust emissions, vehicles in operation, power consumption, green coverage area and built-up area are selected for assessment. Secondly, built-up areas were extracted based on multi-temporal Landsat TM images using a supervised classification method. Finally, the correlation degrees between the population density, exhaust emissions, vehicles in operation, power consumption, green coverage area and, built-up area, and the annual mean temperature, the maximum temperature, the minimum temperature, the seasonal mean temperature and the monthly mean temperature are calculated by the grey relational degree analysis method, respectively. The results show that:(1) During the period of 1983─2011, the temperature of Nanjing are shown as an increasing trend, especially since later period of 1990’s. The absolute temperature increase is 1.5℃ in 1999─2007. (2) It is found that for the same reference sequence (annual mean temperature, monthly mean temperature, etc.), the overall ranking order of the influence factors’ relevance remains no change, i.e., population density>built-up area>exhaust emissions>vehicle in operation>power consumption>green cover area. (3) However, the influence of the same factor to the annual mean temperature, the maximum temperature, the minimum temperature are different. For example, the maximum effect on annual mean temperature is the population density, with the a correlation coefficient of 0.95; the electricity consumption, emissions and vehicle in operation have more significant impacts on annual minimum temperature changes, where the correlation coefficients were 0.68, 0.74, 0.73, respectively. (4) The influence of the same factor on the monthly mean temperature is different. For example, the monthly mean temperature in February and the population density has the minimum correlation coefficient, i.e., 0.78;the monthly mean temperature in March and the population density has the maximum correlation coefficient, and the correlation coefficient is 0.93. (5) With the passage of time, different city factors have the different influence on the change of temperature.