摘要
为探讨“人类活动—大气污染—气温变化”的关系反应链,从宏观尺度阐明PM_(2.5)浓度变化对气温的影响,利用1951—2017年中国822个气象站点日最高气温、日最低气温和日平均气温资料,1998—2016年中国年均PM_(2.5)浓度遥感图像数据、地表太阳辐射数据,1998—2016年中国各省(区)逐年能源消耗总量、地区生产总值及夜间灯光指数数据,运用Slope趋势变化分析方法与相关性分析法,分析了中国PM_(2.5)浓度的变化趋势及其影响因素。结果表明:1998—2016年中国黄淮海区、东北区PM_(2.5)浓度上升速度最快,分别为1.42μg·m^(-3)·a^(-1)、1.44μg·m^(-3)·a^(-1),而其他地区相对变化不明显;黄淮海区PM_(2.5)浓度平均值高,地表太阳辐射降低,对该区年最高气温有明显的抑制作用,但对年平均气温和年最低气温的影响不明显。东北区PM_(2.5)浓度增长速率较高,但年平均浓度值低,该地区有着较高的水热配合度,PM_(2.5)对年最高气温的抑制作用不明显;能源消耗总量与PM_(2.5)浓度呈显著的正相关。
Based on daily maximum temperature,daily minimum temperature,and daily average temperature data from 1951 to 2017 from China′s 822 weather stations,remote sensing image data for annual average PM_(2.5) concentrations in China,surface solar radiation data,annual energy consumption,GDP(gross domestic product)and the night lights index data during 1998-2016,using the slope trend analysis and correlation analysis,the changing trend of PM_(2.5) concentrations in China and its impact factors were analyzed.The results show that the PM_(2.5) concentrations in the Huang-Huai-Hai region and the northeastern part of China have the largest increase rates i.e.1.42μg·m^(-2)·a^(-1) and 1.44μg·m^(-2)·a^(-1) from 1998 to 2016,respectively,while those in the other regions have no obvious change.The average PM_(2.5) concentration in the Huang-Huai-Hai region is high,which reduces the local surface solar radiation and significantly inhibits the local annual maximum temperature,but has no obvious influence on the annual average and minimum temperatures.In northeast China,the annual average PM_(2.5) concentration despite showing a relatively large increase rate is far lower than that in the Huang-Huai-Hai region,and a high hydrothermal coordination degree induces an inconspicuous inhibition effect of PM_(2.5) on the annual maximum temperature.Besides,the total energy consumption is positively correlated with PM_(2.5) concentration.
作者
肖袁俊
李保山
宋文丹
程勇翔
黄敬峰
XIAO Yuan-jun;LI Bao-shan;SONG Wen-dan;CHENG Yong-xiang;HUANG Jing-feng(College of Life Sciences,Shihezi University,Shihezi 832003,China;Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University,Hangzhou 310058,China)
出处
《气象与环境学报》
2022年第3期85-92,共8页
Journal of Meteorology and Environment
基金
欧盟Erasmus+项目“地理信息系统和遥感技术在林业与生态领域的可持续应用”(598838-EPP-1-2018-EL-EPPKA2-CBHE-JP)资助。