中国中东部秋季PM10时空变化及其与日气温的关系

doi:10.11821/xb201209001

地理学报 ›› 2012, Vol. 67 ›› Issue (9): 1155-1164.doi: 10.11821/xb201209001

• 生态与环境 • 下一篇

中国中东部秋季PM₁₀时空变化及其与日气温的关系

郭元喜¹, 龚道溢¹, 汪文珊¹, 张自银², 毛睿¹

1. 北京师范大学, 地表过程与资源生态国家重点实验室, 减灾与应急管理研究院, 北京100875;
2. 北京市气候中心, 北京100089

收稿日期:2012-05-16 修回日期:2012-06-12 出版日期:2012-09-20 发布日期:2012-09-20
作者简介:郭元喜(1983-),女,山东章丘市人,博士研究生,主要从事气候变化研究。E-mail:guoyx@mail.bnu.edu.cn
基金资助:
国家重点基础研究发展计划(973 计划) (2012CB955302); 国家自然科学基金项目(40975043)

Spatiotemporal Variation of PM₁₀ Concentration and Its Relationship with Autumn Daily Temperature over Central and Eastern China

GUO Yuanxi¹, GONG Daoyi¹, WANG Wenshan¹, ZHANG Ziyin², MAO Rui¹

1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China;
2. Beijing Meteorological Bureau, Beijing 100089, China

Received:2012-05-16 Revised:2012-06-12 Online:2012-09-20 Published:2012-09-20
Supported by:
National Basic Research Program of China (973 Program), No.2012CB955302; National Natural Science Foundation of China, No.40975043

摘要/Abstract

摘要： 利用2000-2010 年秋季中国中东部83 个重点城市的PM₁₀浓度数据以及其中63 个城市的逐日气象资料, 分析了PM₁₀浓度的时空变化以及晴空条件下PM₁₀浓度与日气温之间的关系, 讨论了不同云量条件下二者关系的稳定性以及辐射的相应变化。结果表明:(1) 近11 年来, 秋季PM₁₀浓度呈现下降趋势, 全部天气条件下和晴空条件下的线性趋势值分别为-2.87 μg·m^-3/年、-4.92 μg·m^-3/年;空间分布上, 中国中东部重点城市的秋季PM₁₀浓度普遍下降, 其中华北地区的下降最快最显著。(2) 秋季PM₁₀浓度与日气温的波动之间存在显著相关, 定量统计表明:当PM₁₀浓度偏高10 μg·m^-3时, 日最高气温、日最低气温和日平均气温分别偏低0.15℃、0.14℃和0.16℃, 同时气温日较差减小0.01℃。(3) 秋季日气温的上述变化可能主要与气溶胶的直接效应有关。PM₁₀增多会造成地面总辐射和地表净辐射的显著减少, 进而造成日最高气温、日平均气温的显著下降;同时, PM₁₀增多对近地面的影响总体上是致冷效果。

关键词: 中国, 秋季, PM₁₀, 气温, 辐射, 相关分析

Abstract: Based on the autumn data of daily PM₁₀ concentrations in 83 key cities and daily temperature, precipitation and total cloud cover in 63 key cities over Central and Eastern China from 2000 to 2010, the spatiotemporal distribution and variation of PM₁₀ concentration were studied. The results were as follows. (1) During the past 11 years, the averaged PM₁₀ concentration in autumn has showed a relatively steady downward trend on both all days and clear days, with a linear trend of -2.87 μg·m^-3/yr and -4.92 μg·m^-3/yr respectively. As to spatial variation, PM₁₀ concentrations in most cities of Central and Eastern China have decreased significantly. (2) There were significant correlations between PM₁₀ concentrations and daily temperatures in autumn. The quantitative statistics showed that as PM₁₀ concentration increased by each 10 μg·m^-3, the daily maximum temperature, daily minimum temperature, daily mean temperature and diurnal temperature range would decrease by 0.15 ℃, 0.14℃, 0.16℃ and 0.01℃ respectively. (3) The changes of daily temperature in autumn may be caused mainly by direct effect of PM₁₀. When PM₁₀ concentration increased, the surface solar radiation and surface net radiation would decrease significantly, and hence, the daily maximum temperature and daily mean temperature would decrease significantly.

Key words: China, autumn, PM₁₀, temperature, solar radiation, correlation analysis

郭元喜, 龚道溢, 汪文珊, 张自银, 毛睿. 中国中东部秋季PM₁₀时空变化及其与日气温的关系[J]. 地理学报, 2012, 67(9): 1155-1164.

GUO Yuanxi, GONG Daoyi, WANG Wenshan, ZHANG Ziyin, MAO Rui. Spatiotemporal Variation of PM₁₀ Concentration and Its Relationship with Autumn Daily Temperature over Central and Eastern China[J]. Acta Geographica Sinica, 2012, 67(9): 1155-1164.

参考文献

[1] Charlson R J, Schwartz S E, Hales J M et al. Climate forcing by anthropogenic aerosols. Science, 1992, 255(5043):423-430.

[2] Ramanathan V, Crutzen P J, Kiehl J T et al. Aerosols, climate, and the hydrological cycle. Science, 2001, 294(5549):2119-2124.

[3] Kaufman Y J, Tanre D, Boucher O. A satellite view of aerosols in the climate system. Nature, 2002, 419(6903):215-223.

[4] Anderson T L, Charlson R J, Schwartz S E et al. Climate forcing by aerosols: A hazy picture. Science, 2003, 300(5622): 1103-1104.

[5] Seinfeld J H, Pandis S N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. 2nd ed. NewYork: Wiley-Interscience, 2006: 1054-1089.

[6] Levin Z, Cotton W R. Aerosol Pollution Impact on Precipitation: A Scientific Review. Berlin: Springer-Verlag, 2008:205-294.

[7] Menon S, Hansen J, Nazarenko L et al. Climate effects of black carbon aerosols in China and India. Science, 2002, 297(5590): 2250-2253.

[8] Huang Y. Assessments of the direct and indirect effects of anthropogenic aerosols on regional precipitation over EastAsia using a coupled regional climate-chemistry-aerosol model. Atlanta: Georgia Institute of Technology, 2005.

[9] Huang Y, Dickinson R E, Chameides W L et al. Impact of aerosol indirect effect on surface temperature over East Asia.Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(12): 4371-4376.

[10] Zhao Wei, Liu Hongnian, Wu Jian. Radiative and climate effects of dust aerosol in springs over China. Journal ofNanjing University: Natural Sciences, 2008, 44(6): 598-607. [赵伟, 刘红年, 吴涧. 中国春季沙尘气溶胶的辐射效应及对气候影响的研究. 南京大学学报: 自然科学, 2008, 44(6): 598-607.]

[11] Holben B N, Eck T F, Slutsker I et al. AERONET: A federated instrument network and data archive for aerosolcharacterization. Remote Sensing of Environment, 1998, 66(1): 1-16.

[12] Welton E J, Campbell J R, Spinhirne J D et al. Global monitoring of clouds and aerosols using a network ofmicro-pulse lidar systems. Proceedings of the Society of Photo-optical Instrumentation Engineers, 2001, 4153: 151-158.

[13] Murayama T, Sugimoto N, Uno I et al. Ground-based network observation of asian dust events of April 1998 in EastAsia. Journal of Geophysical Research, 2001, 106(D16): 18345-18360.

[14] Mattis I, Ansmann A, Muller D et al. Multiyear aerosol observations with dual-wavelength Raman Lidar in theframework of EARLINET. Journal of Geophysical Research, 2004, 109(D13): 1-15.

[15] Bellouin N, Boucher O, Haywood J et al. Global estimate of aerosol direct radiative forcing from satellitemeasurements. Nature, 2005, 438(7071): 1138-1141.

[16] Chung C E, Ramanathan V, Kim D et al. Global anthropogenic aerosol direct forcing derived from satellite andground-based observations. Journal of Geophysical Research, 2005, 110(D24): 1-17.

[17] Takemura T, Nozawa T, Emori S et al. Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model. Journal of Geophysical Research, 2005, 110(D2): 1-16.

[18] Textor C, Schulz M, Guibert S et al. The effect of harmonized emissions on aerosol properties in global models: AnAer℃om experiment. Atmospheric Chemistry and Physics, 2007, 7(17): 4489-4501.

[19] Clarke A, Kapustin V. Hemispheric aerosol vertical profiles. Science, 2010, 329(5998): 1488-1492.

[20] IPCC. Climate Change 2007: The Physical Science Basis. Cambridge: Cambridge University Press, 2007: 153-180.

[21] NOAA. Aerosols and Climate Fact Sheet. 2011, http://www.nrc.noaa.gov/stateofsciencefactsheets.html.

[22] Gong Daoyi, Han Hui. Extreme climate events over northern China during the last about 50 years. Acta GeographicaSinica, 2004, 59(2): 230-238. [龚道溢, 韩晖. 华北农牧交错带夏季极端气候的趋势分析. 地理学报, 2004, 59(2):230-238.]

[23] Gong D Y, Ho C H, Chen D L et al. Weekly cycle of aerosol-meteorology interaction over China. Journal ofGeophysical Research, 2007, 112(D22): 1-9.

[24] China Meteorological Administration. Specifications for Surface Meteorological Observation. Beijing: ChinaMeteorological Press, 2003: 14. [中国气象局, 地面气象观测规范. 北京: 气象出版社, 2003: 14.]

[25] Qian Y, Wang W, Leung L et al. Variability of solar radiation under cloud-free skies in China: The role of aerosols.Geophysical Research Letters, 2007, 34(12): 1-5.

[26] Shao Zhenyan, Zhou Tao, Shi Peijun et al. Spatial-temporal characteristics of the influence atmospheric pollutant onsurface solar radiation changes for Chinese key cities. Plateau Meteorology, 2009, 28(5): 1105-1114. [邵振艳, 周涛, 史培军等. 大气污染对中国重点城市地面总辐射影响的时空特征. 高原气象, 2009, 28(5): 1105-1114.]

[27] Stone R S, Augustine J A, Dutton E G et al. Empirical determinations of the longwave and shortwave radiativeforcing efficiencies of wildfire smoke. Journal of Geophysical Research, 2011, 116(D12): 1-9.

[28] Sheng Peixuan, Mao Jietai, Li Jianguo et al. Atmospheric Physics. Beijing: Peking University Press, 2009: 72-83. [盛裴轩, 毛节泰, 李建国等. 大气物理学. 北京: 北京大学出版社, 2009: 72-83.]

中国中东部秋季PM₁₀时空变化及其与日气温的关系

Spatiotemporal Variation of PM₁₀ Concentration and Its Relationship with Autumn Daily Temperature over Central and Eastern China

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