基于卫星遥感AOD的华北地区2003—2014年PM<sub>2.5</sub>浓度时空分布特征

陈优芳; 周一敏; 赵昕奕

doi:10.16032/j.issn.1004-4965.2019.074

基于卫星遥感AOD的华北地区2003—2014年PM_2.5浓度时空分布特征

doi: 10.16032/j.issn.1004-4965.2019.074

陈优芳^{1, 2},
周一敏^{1, 2},
赵昕奕^{1, 2, ,}

1.
北京大学城市与环境学院, 北京 100871
2.
地表过程分析与模拟教育部重点实验室, 北京 100871

基金项目:

国家自然科学基金项目《气温时相变化对土地利用/土地覆被变化的响应》 41471073

详细信息

通讯作者:
赵昕奕, 女, 吉林省人, 副教授, 博士, 从事气候学与自然地理研究。E-mail: sh-zhao@urban.pku.edu.cn

中图分类号: X16
计量
- 文章访问数: 220
- HTML全文浏览量: 69
- PDF下载量: 54
- 被引次数: 0
出版历程
- 收稿日期: 2018-10-26
- 修回日期: 2019-09-08
- 刊出日期: 2019-12-01

THE CHARACTERISTICS OF SPATIOTEMPORAL DISTRIBUTION OF HAZE WEATHER IN NORTH CHINA DURING 2003—2014

You-fang CHEN^{1, 2},
Yi-min ZHOU^{1, 2},
Xin-yi ZHAO^{1, 2
, ,}

1.
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
2.
Laboratory For Earth Surface Processes(LESP) Ministry of Education, Beijing 100871, China

摘要

摘要: 华北及周边地区PM_2.5造成的污染, 近十年来引起了社会的广泛关注, 也是科学研究的重要领域。利用2003—2014年的卫星遥感MODIS AOD数据和2014—2015年的地面观测PM_2.5浓度数据, 采用聚类分析、混合效应模型、EOF分解等统计分析方法, 反演了2003—2014年华北及周边地区PM_2.5浓度, 分析其时空分布特征。主要结论如下:(1)卫星遥感MODIS AOD与地面观测PM_2.5值有较高的相关系数, 可利用MODIS卫星遥感AOD对地面观测的PM_2.5浓度进行反演; (2)华北地区PM_2.5浓度呈现出明显的空间分布特征:太行山脉是污染强弱明确的分界线, 山脉东南部的污染显著高于西部, 且在地势变化的地方出现明显的突变; 河北南部、河南北部和山东西北部分区域是污染最严重的地区; (3)2004年、2009年以及2013年后都是污染浓度比较低的年份。
- AOD /
- PM_2.5污染 /
- mixed effect model(混合效应模型) /
- 华北地区 /
- EOF分解
Abstract: In north China, haze pollution has aroused wide concern of the society in the past ten years. Based on satellite remote sensing of aerosol optical thickness (AOD) and ground-based observation of PM_2.5 data, linear regression, cluster analysis, and other statistical analysis methods were used to study the spatiotemporal distribution characteristics. The results showed that: (1) The AOD had higher correlation coefficient with the ground-observed PM_2.5 value, and can be used for the inversion of PM_2.5 in the study area. (2) In north China and the surrounding areas, PM_2.5 presented obvious spatial distribution, the Taihang Mountains was the clear dividing line of the intensity of pollution, contamination of the southeastern mountains was significantly higher than that of the northwest and there were obvious abrupt changes where the terrain varied. Small parts of the southern Hebei Province, northern Henan Province and southern and northwestern parts of Shandong Province were the most polluted areas. (3) Relatively low pollution concentrations happened in 2004, 2009 and the years after 2013.
- MODIS AOD /
- PM_2.5 pollution /
- mixed effect model /
- North China /
- EOF

HTML全文

图 1 研究区及测站城市示意图

下载: 全尺寸图片幻灯片

图 2 空间聚类分析结果行政区切割

下载: 全尺寸图片幻灯片

图 3 混合效应模型中每个城市的拟合系数(R²)点的颜色越深, R²越大。

下载: 全尺寸图片幻灯片

图 4 通过0.10显著性检验的39个城市混合效应模型模拟效果

a. 2014年4—12月的反演PM_2.5浓度和实测PM_2.5浓度拟合情况; b. 2015年1—3月反演PM_2.5浓度和实测PM_2.5浓度拟合情况, 灰线表示拟合的线性回归线。

下载: 全尺寸图片幻灯片

图 5 研究区各个城市在Mixed Effect算法中截距空间插值结果

下载: 全尺寸图片幻灯片

图 6 研究区EOF第一空间模态

下载: 全尺寸图片幻灯片

图 7 整个研究区和9个区域污染日数百分比的年际变化

蓝色系曲线为污染较少的区域, 与左侧坐标相对应; 红色系曲线为污染较多的区域, 与右侧坐标相对应; 黑色曲线为整个研究的污染日数比, 与左侧坐标相对应。

下载: 全尺寸图片幻灯片

表 1 9个区域所在位置

区域编号	区域名称
1	张家口地区
2	赤峰-承德地区
3	辽宁西南地区
4	张家口南部-北京北部地区
5	河北中部-北京南部-天津地区
6	山东中部、山西东南地区
7	河北南部地区
8	山东西南-河南东北地区
9	山东半岛地区

下载: 导出CSV

表 2 不同区域污染情况的相关系数均通过0.01显著性检验。

区域	区域1	区域2	区域3	区域4	区域5	区域6	区域7	区域8	区域9
区域1	1.00	0.67	0.44	0.69	0.56	0.36	0.38	0.21	0.14
区域2	0.67	1.00	0.71	0.78	0.67	0.47	0.46	0.29	0.23
区域3	0.44	0.71	1.00	0.73	0.64	0.50	0.43	0.31	0.38
区域4	0.69	0.78	0.73	1.00	0.82	0.59	0.57	0.35	0.33
区域5	0.56	0.67	0.64	0.82	1.00	0.67	0.72	0.42	0.27
区域6	0.36	0.47	0.50	0.59	0.67	1.00	0.80	0.72	0.59
区域7	0.38	0.46	0.43	0.57	0.72	0.80	1.00	0.69	0.35
区域8	0.21	0.29	0.31	0.35	0.42	0.72	0.69	1.00	0.45
区域9	0.14	0.23	0.38	0.33	0.27	0.59	0.35	0.45	1.00

下载: 导出CSV

参考文献(47)

[1]	LIU X, LI J, QU Y. Formation and evolution mechanism of regional haze: a case study in the megacity Beijing, China[J]. Atmospheric Chemistry And Physics, 2013, 13(9): 4 501-4 514.
[2]	GUO Y, FENG N, CHRISTOPHER S A. Satellite remote sensing of fine particulate matter(PM_2.5) air quality over Beijing using MODIS[J]. International Journal of Remote Sensing, 2014, 35(17): 6 522-6 544.
[3]	JERRETT M. The death toll from air-pollution sources[J]. Nature, 2015, 525(7 569): 330-331.
[4]	KAUFMAN YJ, TANRÉ D, BOUCHER O. A satellite view of aerosols in the climate system[J]. Nature, 2002, 419(6 903): 215-223.
[5]	NEL A. Air pollution-related illness: effect of particals[J]. Science, 2005, 308(5 723):804-806.
[6]	LELIEVELD J, EVANS JS, FNAIS M. The contribution of outdoor air pollution sources to premature mortality on a global scale[J]. Nature, 2015, 525(7 569): 367-371.
[7]	LIM S S, VOS T, FLAXMAN A D. A comparative risk assessment of burden of disease and injury attributableto 67 risk factors and risk factor clusters in 21 regions, 1990—2010: A systematic analysis for the global burdenof disease study 2010[J]. The Lancet, 2012, 380(9 859): 2 224-2 260.
[8]	LEVY R C, REMER L A, KLEIDMAN R G. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land[J]. Atmospheric Chemistry and Physics, 2010, 10(21): 10 399-104 20.
[9]	REMER L A, KAUFMAN Y J, TANRé D. The MODIS aerosol algorithm, products, and Validation[J]. Amer Meteor Soc, 2005, 62(4): 947-973.
[10]	范引琪, 李春强. 1980-2003年京津冀地区大气能见度变化趋势研究[J].高原气象, 2008, 27(6): 1 392-1 401.
[11]	胡亚旦, 周自江.中国霾天气的气候特征分析[J].气象, 2009, 35(7): 73-78.
[12]	吴兑, 吴晓京, 李菲. 1951-2005年中国大陆霾的时空变化[J].气象学报, 2010, 68(5): 680-688.
[13]	邓玉娇, 王捷纯, 何全军.基于MODIS资料分析近15年广州城市热岛特征及演变规律[J].热带气象学报, 2018, 34(6): 755-762.
[14]	陈立, 张杰, 刘振元.基于MODIS云产品的AIRS云相态的识别和云量产品的检验[J].热带气象学报, 2015, 31(4): 486-496.
[15]	邓玉娇, 王捷纯, 曹静.基于MODIS遥感资料监测南海白天雾[J].热带气象学报, 2013, 29(6): 1 046-1 050.
[16]	陈爱军, 周芬, 梁学伟.中国地区MODIS地表反照率反演结果的时空分布研究[J].大气科学学报, 2018, 41(2): 267-274.
[17]	ZHANG Q, JIANG X, TONG D. Transboundary health impacts of transported global air pollution and international trade[J]. Nature, 2017, 543(7 647): 705-709.
[18]	ENGEL-COX J A, HOFF R M, ROGERS R. Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization[J]. Atmos Environ, 2006, 40(40): 8 056-8 067.
[19]	GUPTA P, CHRISTOPHER S A. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach[J]. J Geophy Res, 2009, 114(D20205): 1-14
[20]	LIU Y, PARK R J, JACOB D J. Mapping annual mean ground-level PM_2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States[J]. J Geophy Res: Atmos, 2004, 109(D22206): 1-10.
[21]	VAN DONKELAAR A, MARTIN R V, LEVY R C. Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010[J]. Atmos Environ, 2011, 45(34): 6 225-6 232.
[22]	RAMACHANDRAN S. Aerosol optical depth and fine mode fraction variations deduced from Moderate Resolution Imaging Spectroradiometer (MODIS) over four urban areas in India[J]. J Geophy Res, 2007, 112(D16): D16207-16217.
[23]	林楚勇, 邓玉娇, 徐剑波.基于MODIS的广东省气溶胶光学厚度时空分布特征分析[J].热带气象学报, 2015, 31(6): 821-826.
[24]	许潇锋, 刘晨璇, 唐志伟, 等.基于CALIPSO的华北地区气溶胶垂直分布特征[J].大气科学学报, 2018, 41(1): 126-134.
[25]	LIU Y, SARNAT JA, KILARU V. Estimating Ground-Level PM_2.5 in the Eastern United States using satellite remote sensing[J]. Environmental Science & Technology, 2005, 39(9): 3 269-3 278.
[26]	李本纲, 冉阳, 陶澍.北京市气溶胶的时间变化与空间分布特征[J].环境科学学报, 2008, 28(7): 1 425 -1 429.
[27]	XIE Y, ZHANG Y, XIONG X X. Validation of MODIS aerosol optical depth product over China using CARSNET measurements[J]. Atmos Env, 2011, 45(33): 5 970-5 978.
[28]	焦利民, 张博恩, 许刚.气溶胶光学厚度与PM_2.5浓度相关关系的时空变异[J].干旱区资源与环境, 2016, 30(12): 34-39.
[29]	秦玮, 范广强, 袁琦.南京市近地面颗粒物浓度与MODIS气溶胶产品定量关系研究[J].环境科技, 2016, 29(4): 64-69.
[30]	范萌, 张胜敏, 陈良富.珠三角地区长时间序列气溶胶时空变化特征分析[J].遥感学报, 2016, 20(6): 1 613-1 623.
[31]	KLOOG I, NORDIO F, COULL B A. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM_2.5 exposures in the Mid-Atlantic states[J]. Environmental Science and Technology, 2012, 46(21): 11 913-11 921.
[32]	LIU Y, PACIOREK CJ, KOUTRAKIS P. Estimating regional spatial and temporal variability of PM_2.5 concentrations using satellite data, meteorology, and land use information[J]. Environ Health Perspect, 2009, 117(6): 886-892.
[33]	WANG J. Intercomparison between satellite-derived aerosol optical thickness and PM_2.5 mass: Implications for air quality studies[J]. Geophy Res Lett, 2003, 30 (21): 267-283.
[34]	LIU Y, FRANKLIN M, KAHN R. Using aerosol optical thickness to predict ground-level PM_2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS[J]. Remote Sensing of Environment, 2007, 107(2): 33-44.
[35]	HENDERSON C R. Estimation of general specific and maternal combining abilities[J]. Nida Research Monograph, 1948, 37: 241-270.
[36]	LEE H J, LIU Y, COULL B A. A novel calibration approach of MODIS AOD data to predict PM_2.5 concentrations[J]. Atmospheric Chemistry and Physics, 2011, 11(15): 7 991-8 002.
[37]	MA Z, LIU Y, ZHAO Q. Satellite-derived high resolution PM_2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model[J]. Atmos Environ, 2016, 133: 156-164.
[38]	康娜, 辛金元, 蔺永耀.北京山前典型细粒子污染过程的气象条件分析[J].环境科学研究, 2009, 22(9): 1 013-1 020.
[39]	杨雨灵, 谭吉华, 孙家仁.华北地区一次强灰霾污染的天气学效应[J].气候与环境研究, 2015, 20(5): 555-570.
[40]	XIE Y, WANG Y, ZHANG K. Daily Estimation of Ground-Level PM_2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD[J]. Environmental Science and Technology, 2015, 49(20): 12 280-12 288.
[41]	LORENZ E N. Emprical orthogonal functions and statistical weather prediction[M]. Cambridge: Statistical Forecasting Project Department of Meteorology, MIT, 1956: 49.
[42]	曹国良, 张小曳, 龚山陵.中国区域主要颗粒物及污染气体的排放源清单[J].中国科学, 2011, 56(8): 781-788.
[43]	ZHANG Q, STREETS D G, CARMICHAEL G R. Asian emissions in 2006 for the NASA INTEX-B mission[J]. Atmospheric Chemistry and Physics, 2009, 9(14): 5 131-5 153.
[44]	BARNES W L, PAGANO T S, SALOMONSON V V. Prelaunch Characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1[J]. IEEE Transactions on Geosciences & sremote Sensing, 1998, 36(4): 1 088-1 100.
[45]	李璇, 聂滕, 齐珺. 2013年1月北京市PM_2.5区域来源解析[J].环境科学, 2015, 36(4): 1 148-1 153.
[46]	汤静, 王春林, 谭浩波, 等.利用PCA-kNN方法改进广州市空气质量模式PM_2.5预报[J].热带气象学报, 2019, 35(1): 125-134.
[47]	钱俊龙, 牛文胜, 查书瑶, 等.外来输送对江苏冬季PM_2.5贡献的数值模拟研究[J].热带气象学报, 2019, 35(5):673-680.