Spatial Distribution Model of Countrywide PM<sub>2.5</sub> Concentration and Influence Factors Using Geographical Information System

LI Song; LI Lian; LI Lian; WANG Hongyan

doi:10.13961/j.cnki.stbctb.2015.04.038

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Spatial Distribution Model of Countrywide PM_2.5 Concentration and Influence Factors Using Geographical Information System

- Spatial Distribution Model of Countrywide PM_2.5 Concentration and Influence Factors Using Geographical Information System
- Bulletin of Soiland Water Conservation Vol. 35, Issue 4, Pages: 202-205(2015)
- 作者机构：
  
  1. 贵州师范学院资源环境与灾害研究所,贵州,贵阳,550018
  2. 中国科学院遥感与数字地球研究所,北京,100101
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2015.04.038
  CLC：
- Published：2015
- 稿件说明：
移动端阅览
LI Song, LI Lian, LI Lian, et al. Spatial Distribution Model of Countrywide PM_2.5 Concentration and Influence Factors Using Geographical Information System[J]. Bulletin of Soiland Water Conservation, 2015, 35(4): 202-205.
DOI：

LI Song, LI Lian, LI Lian, et al. Spatial Distribution Model of Countrywide PM_2.5 Concentration and Influence Factors Using Geographical Information System[J]. Bulletin of Soiland Water Conservation, 2015, 35(4): 202-205. DOI： 10.13961/j.cnki.stbctb.2015.04.038.

摘要

[目的

]

研究中国PM

2.5

的空间分布特征及其影响因素

为区域可持续发展提供科学依据。[方法

]

利用2014年2月25日上午9时和3月23日9时来自国家环保部的PM

2.5

时均浓度值

以GIS为平台利用双三次B样条方法

以中国陆疆国界为内插区域

模拟两个时相PM

2.5

浓度的空间分布

并在此基础上对比分析了中国和美国PM

2.5

浓度标准的差异

进一步分析荒漠化、降水、风速和经济增长水平对PM

2.5

浓度空间分异的影响。[结果

]

模拟结果表明

京、津为中心的华北地区是中国PM

2.5

污染严重的区域

珠三角是另一个污染较严重的区域

西藏、新疆和贵州等西部省区是中国PM

2.5

浓度较低

空气质量较好的区域。[结论

]

我国各地区PM

2.5

浓度与区域经济发展水平表现出显著的相关性。

Abstract

[Objective] The characteristics of spatial distribution of PM2.5 in China and the influence factors were studied to provide scientific basis for environment monitoring. [Methods] This paper collected hourly concentrations of PM2.5 pollutant at 9:00 on February 25 th and 9:00 on March 23 th

2014. Consequentially

the countrywide spatial distribution of PM2.5 concentration was simulated within national boundaries using bicubic B-spline method in GIS. The concentration distribution was compared with that of USA spatially atdifferent standard. [Results] The most serious polluted region is Beijing and Tianjin-centered north China

and another is Pearl River Delta. The western provinces

including Tibet

Xinjiang and Guizhou area are good-air regions with low concentration. [Conclusion] There is a stable relationship between economic growth and PM2.5 concentration.

关键词

Keywords

references

Guo Jianping, Zhang Xiaoye, Wu Yerong, et al. Spatio-temporal variation trends of satellite-based aerosol optical depth in China during 1980-2008[J]. Atmospheric Environment, 2011, 45(37):6802-6811.

王敏,周滨,郭宇,等.基于BP人工神经网络的城市PM

2.5

浓度空间预测[J].环境污染与防治,2013,35(9):63-66.

蒲维维,赵秀娟,张小玲.北京地区夏末秋初气象要素对PM

2.5

污染的影响[J].应用气象学报,2011,22(6):716-723.

孟晓艳,魏桢,王瑞斌,等.灰霾试点城市PM

2.5

浓度特征及其影响因素分析[J].环境科学与技术,2013,36(9):76-80.

李龙凤,王新明,赵利容,等.广州市街道环境PM

和PM

2.5

质量浓度的变化特征[J].地球与环境,2005,33(2):57-60.

于建华,虞统,魏强,等.北京地区PM

和PM

2.5

质量浓度的变化特征[J].环境科学研究,2004,17(1):45-47.

苏彬彬,刘心东,陶俊.华东区域高山背景点PM

和PM

2.5

背景值及污染特征[J].环境科学,2013,34(2):455-461.

Karnae Saritha, John Kuruvilla. Source apportionment of fine particulate matter measured in an industrialized coastal urban area of South Texas[J]. Atmospheric Environment, 2011,45(23):3769-3776.

Xu Lingling, Chen Xiaoqiu, Chen Jinshen, et al. Seasonal variations and chemical compositions of PM

2.5

aerosol in the urban area of Fuzhou, China[J]. Atmospheric Research, 2012, 104(3/4):264-272.

黄虹,李顺诚,曹军骥,等.广州市住宅室内PM

2.5

排放源的定量计算[J].华南师范大学学报:自然科学版,2007(1):64-69.

王泰,陈曦,何公理,等.北京市城区冬季雾霾天气PM

2.5

中元素特征研究[J].光谱学与光谱分析,2013,33(6):1441-1445.

时彦玲,邓林红.细颗粒物(PM

2.5

)对气道的病理作用及其与哮喘病理机制的关系[J].医用生物力学,2013,28(2):127-134.

谢元博,陈娟,李巍.雾霾重污染期间北京居民对高浓度PM

2.5

持续暴露的健康风险及其损害价值评估[J].环境科学,2013,35(1):1-8.

殷永文,程金平,段玉森,等.上海市霾期间PM

2.5

,PM

污染与呼吸科、儿呼吸科门诊人数的相关分析[J].环境科学,2011,32(7):1894-1898.

谢元博,陈娟,李巍.雾霾重污染期间北京居民对高浓度PM

2.5

持续暴露的健康风险及其损害价值评估[J].环境科学,2014,35(1):1-8.

Ma Yanjun, Chen Renjie, Pan Guowei, et al. Fine particulate air pollution and daily mortality in Shenyang, China[J]. Science of the Total Environment, 2011,409(13):2473-2477.

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⁰

Spatial Distribution Model of Countrywide PM2.5 Concentration and Influence Factors Using Geographical Information System

DOI：10.13961/j.cnki.stbctb.2015.04.038

摘要

Abstract

关键词

Keywords

references

Spatial Distribution Model of Countrywide PM_2.5 Concentration and Influence Factors Using Geographical Information System