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遥感技术与应用  2020, Vol. 35 Issue (4): 845-854    DOI: 10.11873/j.issn.1004-0323.2020.4.0845
甘肃遥感学会专栏     
2015年中国PM2.5浓度遥感估算与时空分布特征
魏石梅(),潘竟虎(),妥文亮
西北师范大学 地理与环境科学学院,甘肃 兰州 730070
Estimation and Spatial-temporal Distribution Characteristics of PM2.5 Concentration by Remote Sensing in China in 2015
Shimei Wei(),Jinghu Pan(),Wenliang Tuo
College of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070, China
 全文: PDF(4522 KB)   HTML
摘要:

以PM2.5污染物为主的大气污染对社会的可持续发展及人类健康带来了严峻的挑战,厘清我国PM2.5污染物的空间分布特征及演变规律,对于PM2.5污染物的区域联防联控具有重要的意义。基于MODIS卫星的气溶胶产品、气象基础数据以及PM2.5污染物实测站点监测数据,构建地理加权回归模型,对2015年中国PM2.5污染物浓度进行了模拟估算,对PM2.5污染物浓度的空间分异格局及季节演化特征进行分析。结果表明:①2015年全国PM2.5浓度整体表现出明显的空间地带性分异特征。北方PM2.5污染物浓度明显高于南方,中部明显高于东部与西部;②4个季度PM2.5浓度表现出明显的季节适应性演化特征。第四季度PM2.5污染最重,第三季度和第一季度次之,第二季度最低,最大值出现在第四季度(165 μg/m3),最小值出现在第二季度(4.3 μg/m3)。③通过多因子构建的地理加权回归模型估算的PM2.5浓度具有较高的模拟精度,第一至第四季度的相对误差分别为10.2%、7.0%、9.3%和8.6%。

关键词: PM2.5MODIS遥感估算时空分布中国    
Abstract:

Air pollution characterized by PM2.5 pollutants poses severe challenges to the sustainable development of society and human health. Therefore, it is of great significance to clarify the spatial-temporal distribution and evolution of PM2.5 pollutants in China for regional joint prevention and control of PM2.5 pollutants. Based on the MODIS satellite aerosol products, meteorological basic data and PM2.5 pollutant monitoring site monitoring data, a geographically weighted regression model was established to simulate and estimate PM2.5 pollutant concentration in China in 2015 on the basis of aerosol and meteorological data pre-processing. In addition, the spatial distribution pattern, the seasonal evolution characteristics of PM2.5 pollutant concentration were analyzed. The results showed that: (1) the PM2.5 concentration values in China in 2015 as a whole showed obvious spatial zonal differentiation characteristics. The concentration of pollutants in the north is significantly higher than that in the south, and the areas with high PM2.5 concentrations are mainly concentrated in the Beijing-Tianjin-Hebei region, the Jianghuai plain, the Sichuan basin, and the Takaramalkan desert. The area has a wide spatial distribution and significant continuity; (2) The PM2.5 concentration in the fourth quarter showed obvious seasonal adaptive evolution characteristics. The PM2.5 concentration changed significantly in the season. PM2.5 pollution was the heaviest in the fourth quarter, followed by the first quarter of the third quarter and the lowest in the second quarter. The maximum occurred in the fourth quarter (165 μg/m3). The minimum appeared in the second quarter (4.3 μg/m3). Seasonal changes in PM2.5 concentrations were influenced by meteorological factors and human social activities; and (3) The accuracy of the inversion of PM2.5concentration by a multi-factorial, geographically weighted regression model was higher, with relative errors in the four quarters being 10.2%, 7.0%, 9.3%, and 8.6%, respectively.

Key words: PM2.5    MODIS    Remote sensing estimation    Spatio-temporal distribution    China
收稿日期: 2019-09-03 出版日期: 2020-09-15
ZTFLH:  X513  
基金资助: 国家自然科学基金项目(41661025);西北师范大学青年教师科研能力提升计划(NWNU?LKQN?16?7)
通讯作者: 潘竟虎     E-mail: nwnuweism@126.com;panjh_nwnu@nwnu.edu.cn
作者简介: 魏石梅(1993-),女,宁夏固原人,硕士研究生,主要从事生态环境遥感研究。E?mail:nwnuweism@126.com
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引用本文:

魏石梅, 潘竟虎, 妥文亮. 2015年中国PM2.5浓度遥感估算与时空分布特征[J]. 遥感技术与应用, 2020, 35(4): 845-854.

Shimei Wei, Jinghu Pan, Wenliang Tuo. Estimation and Spatial-temporal Distribution Characteristics of PM2.5 Concentration by Remote Sensing in China in 2015. Remote Sensing Technology and Application, 2020, 35(4): 845-854.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0845        http://www.rsta.ac.cn/CN/Y2020/V35/I4/845

图1  2015年全国PM2.5地面监测站点分布图(审图号:GS(2016)2885)
图2  2015年全国气象监测站点分布图(审图号:GS(2016)2885)
气象因子季度最大值最小值平均数
气压/Pa第一季度10 160.335 728.679 171.40
第二季度10 077.675 728.679 120.07
第三季度10 206.335 756.339 211.47
第四季度10 299.335 655.469 244.34
风速/m·s-1第一季度97.677.6724.85
第二季度96.004.9221.34
第三季度79.674.3320.13
第四季度10 956.334.6734.49
相对湿度/%第一季度93.6716.0060.06
第二季度93.6726.0069.41
第三季度94.0020.0070.49
第四季度87.0012.0064.69
降水量/mm第一季度4 401.670.00726.11
第二季度5 949.6724.001 348.79
第三季度10 960.330.00709.90
第四季度2 303.000.00356.78
表1  各季度气象数据描述性统计结果
图3  各个季度PBLH插值结果(审图号:GS(2016)2885)
第一季度第二季度第三季度第四季度
决定系数(R20.530.560.580.54
相对误差10.2%7.0%9.3%8.6%
表2  模型检验统计结果
图4  2015年PM2.5空间分布(审图号:GS(2016)2885)
图5  2015年各个季度PM2.5空间分布(审图号:GS(2016)2885)
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