Utilization of MERSI and MODIS data to monitor PM<sub>2.5</sub> concentration in Beijing–Tianjin–Hebei and its surrounding areas

Hui CHEN; Qing LI; Zhongting WANG; Yun SUN; Huiqin MAO; Bin CHENG

doi:10.11834/jrs.20187123

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Utilization of MERSI and MODIS data to monitor PM_2.5 concentration in Beijing–Tianjin–Hebei and its surrounding areas
Vol. 22, Issue 5, Pages: 822-832(2018)
Received：03 May 2017，

Accepted：23 February 2018，

Published：2018-09
DOI： 10.11834/jrs.20187123
稿件说明：

移动端阅览

陈辉, 厉青, 王中挺, 孙云, 毛慧琴, 程斌. 2018. MERSI和MODIS卫星监测京津冀及周边地区PM_2.5浓度. 遥感学报, 22(5): 822–832 DOI： 10.11834/jrs.20187123.

Chen H, Li Q, Wang Z T, Sun Y, Mao H Q and Cheng B. 2018. Utilization of MERSI and MODIS data to monitor PM_2.5 concentration in Beijing–Tianjin–Hebei and its surrounding areas. Journal of Remote Sensing, 22(5): 822–832 DOI： 10.11834/jrs.20187123.

摘要

京津冀及周边地区是中国PM

2.5

污染最重的区域之一，利用卫星遥感技术监测大范围的PM

2.5

时空分布变化是一种先进的重要手段。本研究首先基于暗像元算法利用FY-3B/MERSI与AQUA/MODIS对京津冀及周边区域进行了遥感AOT反演和验证分析；然后，引入气象资料和地面观测资料利用GWR模型反演了区域PM

2.5

浓度，并对遥感反演结果进行了交叉验证评估，综合对比分析了MERSI和MODIS的气溶胶及PM

2.5

遥感监测能力；最后，利用MERSI数据对2017年第一季度京津冀及周边区域的PM

2.5

月均浓度时空分布变化情况进行了初步探索分析。结果表明：FY-3B/MERSI在气溶胶及PM

2.5

遥感监测能力方面略优于AQUA/MODIS，MERSI反演的1 km分辨率AOT和PM

2.5

与地面站点实测结果的决定系数

分别为0.76 μg/m

和0.79 μg/m

，均方根误差分别为0.26 μg/m

和28 μg/m

，平均绝对误差分别为0.16 μg/m

和15 μg/m

，能基本满足对京津冀及周边区域PM

2.5

的精细化监测需要。2017年第一季度京津冀及周边区域PM

2.5

月均浓度遥感监测结果表明该区域的PM

2.5

空间分布格局与地形地貌关系密切，高值区整体上沿太行山脉成带成片；从时间变化来看，1—3月呈逐月下降的趋势，其中3月份PM

2.5

区域浓度较1月和2月有大幅下降。这说明FY-3\MERSI遥感反演产品能为环境质量监测和环境管理工作效果评估提供有效参考，本研究对国产卫星在大气环境遥感业务中的大力发展应用有重要参考意义。

Abstract

Beijing–Tianjin–Hebei and its surrounding areas are some of the most PM

2.5

-polluted regions in China. Satellite remote sensing technology is an advanced and important means for monitoring the change in spatio-temporal distribution of large-range PM

2.5

. In this study

we conduct AOT retrieval and validation analysis based on the Dark Target (DT) Method by utilizing FY-3B/MERSI and AQUA/MODIS satellite data in this region. The weather and ground observation data are brought in to retrieve the regional PM

2.5

concentration using the GWR model

and cross-verification assessment for remote sensing and retrieval results is conducted. Through comprehensive comparison

this study analyzes the capability of MERSI and MODIS in monitoring aerosol and PM

2.5

. Finally

a preliminary exploration analysis on the monthly temporal and spatial changing status of PM

2.5

concentration is conducted in the first quarter of 2017 in Beijing–Tianjin–Hebei and its surrounding areas by utilizing MERSI data. Results show that the remote sensing monitoring capability of FY-3B/MERSI is slightly better than that of AQUA/MODIS. The

between AOT and PM

2.5

dataset with a resolution of 1 km retrieved from MERSI and that from the ground station observation results are 0.76 and 0.79

respectively. The root-mean-square errors are 0.26 and 28 μg/m

respectively

while the mean absolute errors are 0.16 and 15 μg/m

respectively. The results can basically meet the demand of fine PM

2.5

monitoring in Beijing–Tianjin–Hebei and its surrounding areas. The remote sensing monitoring results of monthly PM

2.5

concentration in the first quarter of 2017 in Beijing–Tianjin–Hebei and its surrounding areas show that the spatial pattern of PM

2.5

is closely related to the terrain and landscape

with the high concentration zone mainly lying along the Taihang mountains in flakes. From the view of temporal change

a decreasing trend is noted

with March seeing a plunge in concentration value compared with the first two months. The findings suggest that FY-3B/MERSI remote sensing retrieval results can provide effective reference for environmental quality monitoring and environmental management effectiveness evaluation. This study is significant for the development of domestic satellite application in atmospheric environmental remote sensing sector.

关键词

Keywords

references

陈辉, 厉青, 王中挺, 毛慧琴, 周春艳, 张丽娟, 晁雪林. 2014. 利用MODIS资料监测京津冀地区近地面PM 2.5 方法研究. 气象与环境学报, 30(5): 27–37

Chen H, Li Q, Wang Z T, Mao H Q, Zhou C Y, Zhang L J and Chao X L. 2014. Study on monitoring surface PM 2.5 concentration in Jing-Jin-Ji regions using MODIS data. Journal of Meteorology and Environment, 30(5): 27–37 (

陈辉, 厉青, 张玉环, 周春艳, 王中挺. 2016. 基于地理加权模型的我国冬季PM 2.5 遥感估算方法研究. 环境科学学报, 36(6): 2142–2151

Chen H, Li Q, Zhang Y H, Zhou C Y and Wang Z T. 2016. Estimations of PM 2.5 concentrations based on the method of geographically weighted regression. Acta Scientiae Circumstantiae, 36(6): 2142–2151 (

杜德艳, 李金娟, 陶芸, 程佳惠, 张维. 2015. 贵阳市PM 2.5 微观特征的季节变化分析. 环境科学学报, 35(6): 1645–1650

Du D Y, Li J J, Tao Y, Cheng J H and Zhang W. 2015. Seasonal variations of microscopic characteristics of PM 2.5 in Guiyang City. Acta Scientiae Circumstantiae, 35(6): 1645–1650 (

Hoff R M and Christopher S A. 2009. Remote sensing of particulate pollution from space: have we reached the promised land?. Journal of the Air and Waste Management Association, 59(6): 645–675

胡鸣, 张懿华, 赵倩彪. 2015. 上海市冬季PM 2.5 无机元素污染特征及来源分析. 环境科学学报, 35(7): 1993–1999

Hu M, Zhang Y H and Zhao Q B. 2015. Characteristics and sources of inorganic elements in PM 2.5 during wintertime in Shanghai. Acta Scientiae Circumstantiae, 35(7): 1993–1999 (

Hu X F, Waller L A, Al-Hamdan M Z, Crosson W L, Estes M G Jr, Estes S M, Quattrochi D A, Sarnat J A and Liu Y. 2013. Estimating ground-level PM 2.5 concentrations in the southeastern U.S. using geographically weighted regression. Environmental Research, 121: 1–10

贾松林, 苏林, 陶金花, 王子峰, 陈良富, 尚华哲. 2014. 卫星遥感监测近地表细颗粒物多元回归方法研究. 中国环境科学, 34(3): 565–573

Jia S L, Su L, Tao J H, Wang Z F, Chen L F and Shang H Z. 2014. A study of multiple regression method for estimating concentration of fine particulate matter using satellite remote sensing. China Environmental Science, 34(3): 565–573 (

R. C. Levy, L. A. Remer, D. Tanré, S. Mattoo, and Y. J. Kaufman, 2009, Algorithm for remote sensing of tropospheric aerosols over dark targets from MODIS, Collections 005 and 051: Revision 2, Feb 2009, Product ID: MOD04/MYD04, pp: 1–96.

Li C C, Lau A K H, Mao J T and Chu D A. 2005. Retrieval, validation, and application of the 1 km aerosol optical depth from MODIS measurements over Hong Kong. IEEE Transactions on Geoscience and Remote Sensing, 43(11): 2650–2658

刘俊, 安兴琴, 朱彤, 翟世贤, 李楠. 2014. 京津冀及周边减排对北京市PM 2.5 浓度下降评估研究. 中国环境科学, 34(11): 2726–2733

Liu J, An X Q, Zhu T, Zhai S X and Li N. 2014. Evaluation of PM 2.5 decrease in Beijing after emission restrictions in the Beijing-Tianjin-Hebei and surrounding regions. China Environmental Science, 34(11): 2726–2733 (

Liu Y, Paciorek C J and Koutrakis P. 2009. Estimating regional spatial and temporal variability of PM 2.5 concentrations using satellite data, meteorology, and land use information. Environmental Health Perspectives, 117(6): 886–892

Ma Z, Hu X, Huang L, Bi J, Liu Y. 2014. Estimating ground-level PM 2.5 in China using satellite remote sensing. Environ Sci Technol, 48: 7436–7444

马宗伟. 2015. 基于卫星遥感的我国PM 2.5 时空分布研究. 南京: 南京大学

Ma Z W. 2015. Study on Spatiotemporal Distributions of PM 2.5 in China Using Satellite Remote Sensing. Nanjing: Nanjing University

Rodríguez J D, Peréz A and Lozano J A. 2010. Sensitivity analysis of k -fold cross validation in prediction error estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3): 569–575

陶金花, 张美根, 陈良富, 王子峰, 苏林, 葛萃, 韩霄, 邹铭敏. 2013. 一种基于卫星遥感AOT估算近地面颗粒物的方法. 中国科学: 地球科学, 43(1): 143–154

Tao J H, Zhang M G, Chen L F, Wang Z F, Su L, Ge C, Han X and Zou M M. 2013. A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness. Science China Earth Sciences, 43(1): 143–154 (

Wang Z F, Chen L F, Tao J H, Zhang Y and Su L. 2010. Satellite-based estimation of regional particulate matter (PM) in Beijing using Vertical-and-RH correcting method. Remote Sensing of Environment, 114(1): 50–63

谢杨, 戴瀚程, 花岡達也, 増井利彦. 2016. PM 2.5 污染对京津冀地区人群健康影响和经济影响. 中国人口·资源与环境, 26(11): 19–27

Xie Y, Dai H C, Hanaoka T and Masui T. 2016. Health and economic impacts of PM 2.5 pollution in Beijing-Tianjin-Hebei Area. China Population Resources and Environment, 26(11): 19–27 (

杨洪斌, 邹旭东, 汪宏宇, 刘玉彻. 2012. 大气环境中PM 2.5 的研究进展与展望. 气象与环境学报, 28(3): 77–82

Yang H B, Zou X D, Wang H Y and Liu Y C. 2012. Study progress on PM 2.5 in atmospheric environment. Journal of Meteorology and Environment, 28(3): 77–82 (

杨何群, 尹球, 周红妹, 葛伟强. 2012. 利用MATLAB实现FY-3/MERSI地表温度反演及专题制图. 国土资源遥感(4): 62–70

Yang H Q, Yin Q, Zhou H M and Ge W Q. 2012. Utilization of MATLAB to realize LST retrieval and thematic mapping from FY-3/MERSI data. Remote Sensing for Land and Resources(4): 62–70 (

张莹, 李正强. 2013. 利用细模态气溶胶光学厚度估计PM 2.5 . 遥感学报, 17(4): 929–943

Zhang Y and Li Z Q. 2013. Estimation of PM 2.5 from fine-mode aerosol optical depth. Journal of Remote Sensing, 17(4): 929–943 (

周永波, 白洁, 周著华, 齐琳琳. 2014. FY-3A/MERSI海上沙尘天气气溶胶光学厚度反演. 遥感学报, 18(4): 771–787

Zhou Y B, Bai J, Zhou Z H and Qi L L. 2014. Aerosol optical depth retrieval from FY-3A/MERSI for sand-dust weather over ocean. Journal of Remote Sensing, 18(4): 771–787 (

祝必琴, 黄淑娥, 陈兴鹃, 樊建勇. 2014. 基于FY3B/MERSI水稻长势监测及其与AQUA/MODIS数据对比分析. 江西农业大学学报, 36(5): 1009–1015

Zhu B Q, Huang S E, Chen X J and Fan J Y. 2014. Monitoring of rice growth based on FY3B/MERSI with AQUA/MODIS data contrastive analysis. Acta Agriculturae Universitatis Jiangxiensis, 36(5): 1009–1015 (

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