收稿：2016-09-05，
纸质出版：2017-03
移动端阅览
为研究城市夜间PM
2.5
质量浓度，利用NPP卫星上可见光红外成像辐射套件(VIIRS)DNB通道的微光辐射数据，以辐射传输理论为基础，建立了夜间城市灯光辐射强度与地面表层PM
2.5
质量浓度的关系，并基于支持向量机方法建立了夜间城市PM
2.5
质量浓度反演模型。以北京市作为研究对象，选取2015-03—2015-05期间无月、无云且晴朗夜空条件下4个PM
2.5
监测站点的微光辐射数据与时空匹配的PM
2.5
质量浓度数据对模型进行验证。研究结果表明：夜间城市灯光辐射强度与地面表层PM
2.5
质量浓度呈现负相关性，相关系数最高的定陵站点达到–0.83。基于支持向量机方法建立的PM
2.5
反演模型获得的PM
2.5
质量浓度与实际PM
2.5
质量浓度的相关系数达到0.95，反演结果较优，为进一步大范围监测PM
2.5
质量浓度空间分布以及改善城市夜间空气质量状况评估方法提供了可行性参考。
Particulate matter with an aerodynamic diameter of less than 2.5 μm (PM
2.5
) can be transmitted for long distances and remain in the air for a long period of time
which can cause haze pollution. The accurate monitoring of PM
2.5
mass concentration
especially nighttime PM
2.5
mass concentration
has notable significance in ambient air quality
traffic safety
and human health. This study focuses on monitoring PM
2.5
based on radiative transfer theory by utilizing nighttime radiance data collected by the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite. Moonlight and artificial lights are the major sources of visible light at night. Nighttime city light imagery has certain instruction ability to ambient air quality. This study adopts the Support Vector Machine (SVM) method to establish the PM
2.5
mass concentration inversion model. Nighttime city light intensity was selected as the input parameters for the SVM model. Spatially and temporally paired surface PM
2.5
data was selected as the output parameter. This study focuses on the moonless and cloudless nights in Beijing
China from March 2015 to May 2015. First
the contrast of the DNB images qualitatively shows that the DNB imagery is sensitive to the PM
2.5
changes at night. Starting from the Beer's law
this study then establishes a link between the nighttime surface PM
2.5
mass concentration and the city light intensity radiance measured by the DNB. Second
the correlation coefficient between PM
2.5_rh
/
μ
and ln(
I
) is calculated at each PM
2.5
site following the link. Results show a negative correlation
with the largest correlation reaching –0.83 at the Dingling site. This scenario reflects a higher PM
2.5
mass concentration in the surface air
and the city light radiance attenuates more in the atmospheric transmission path. Finally
the cross validation of the SVM model shows a linear correlation of 0.95 with respect to the corresponding surface observation PM
2.5
mass concentration and a best-fit equation of
y
=0.98
x
–1.82. The average absolute deviation of the SVM model and observed values is 4.89 μg·m
–3
whereas the least absolute deviation is only 0.58 μg·m
–3
. Most of the relative deviation is less than 10%
and the minimum relative deviation is only 0.25%. Furthermore
a statistical analysis illustrates the surface PM
2.5
mass concentration at the VIIRS night overpass (~2:00 AM in Beijing) time is representative of the daily-mean PM
2.5
during the 3-month period. This study provides a feasible method of PM
2.5
inversion utilizing DNB nighttime data. The study results indicate the accuracy of the SVM model. This model is largely significant in further filling the temporal and spatial gaps of nighttime PM
2.5
monitoring
which can significantly advance the research on PM
2.5
effects on the weather
environment
and human health.
Chu D A, Tsai T C, Chen J P, Chang S C, Jeng Y J, Chiang W L and Lin N H. 2013. Interpreting aerosol lidar profiles to better estimate surface PM 2.5 for columnar AOD measurements. Atmospheric Environment, 79:172–187.
方荀. 2015. VIIRS白天/夜晚波段数据应用概况. 科技创新导报, 12(12): 42–43
Fang X. 2015. VIIRS day/night band data application.Science and Technology Innovation Herald, 12(12): 42–43
郭建平, 吴业荣, 张小曳, 李小文. 2013. BP网络框架下MODIS气溶胶光学厚度产品估算中国东部PM 2.5 . 环境科学, 34(3): 817–825
Guo J P, Wu Y R, Zhang X Y and Li X W. 2013. Estimation of PM 2.5 over Eastern China from MODIS aerosol optical depth using the back propagation neural network.Environmental Science, 34(3): 817–825
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
黄辉军, 刘红年, 蒋维楣, 黄世鸿, 张予燕. 2002. 南京市PM 2.5 物理化学特性及来源解析. 气候与环境研究, 11(6): 713–722
Huang H J, Liu H N, Jiang W M, Huang S H and Zhang Y Y. 2002. Physical and chemical characteristics and source apportionment of PM 2.5 in Nanjing.Climatic and Environmental Research, 11(6): 713–722
Kahn R, Banerjee P, McDonald D and Diner D J. 1998.Sensitivity of multiangle imaging to aerosol optical depth and to pure-particle size distribution and composition over ocean. Journal of Geophysical Research: Atmospheres, 103(D24): 32195–32213
Li C C, Mao Y T, Lau A K H, Yuan Z B, Wang M H and Liu X Y. 2005. Application of MODIS satellite products to the air pollution research in Beijing.Science China Earth Sciences, 48(S2): 209–219
Li R Y, Liu X N and Li X Q. 2015.Estimation of the PM 2.5 pollution levels in Beijing based on nighttime light data from the defense meteorological satellite program-operational Linescansystem. Atmosphere, 6(5): 607–622
刘洁, 张小玲, 徐晓峰, 徐宏辉. 2008. 北京地区SO 2 、NO x 、O 3 和PM 2.5 变化特征的城郊对比分析. 环境科学, 29(4): 1059–1065
Liu J, Zhang X L, Xu X F and Xu H H. 2008. Comparison analysis of variation characteristics of SO 2 , NO x , O 3 and PM 2.5 between rural and urban areas, Beijing.Environmental Science, 29(4): 1059–1065
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: 8–52
Ma Z W. 2015.Study on Spatiotemporal Distributions of PM 2.5 in China Using Satellite Remote Sensing. Nanjing: Nanjing University: 8–52
孟燕军, 程丛兰. 2002. 影响北京大气污染物变化的地面天气形势分析. 气象, 28(4): 42–47
Meng Y J and Cheng C L. 2002.Impact of surface synoptic situations on air pollution in Beijing area.Meteorological Monthly, 28(4): 42–47
Miller S D and Turner R E. 2009.A dynamic lunar spectral irradiance data set for NPOESS/VIIRS Day/Night band nighttime environmental applications.IEEE Transactions on Geoscience andRemote Sensing, 47(7): 2316–2329
宋堃, 高太长, 刘西川, 印敏, 薛杨. 2015. 基于支持向量机的微波链路雨强反演方法. 物理学报, 64(24): 0244301
Song K, Gao T C, Liu X C, Yin M and Xue Y. 2015. Method and experiment of rainfall intensity inversion using a microwave link based on support vector machine. ActaPhysicaSinica, 64(24): 0244301
Van Donkelaar A, Martin R V and Park R J. 2006. Estimating ground-level PM 2.5 using aerosol optical depth determined from satellite remote sensing. Journal of Geophysical Research, 111(D21): D21201
Wang J, Aegerter C, Xu X G and Szykman J J. 2016. Potential application of VIIRS Day/Night Bandfor monitoring nighttime surface PM 2.5 air quality from space. Atmospheric Environment, 124: 55–63
Wang J and Christopher S A. 2003.Intercomparison between satellite-derived aerosol optical thickness and PM 2.5 mass: implications for air quality studies. Geophysical Research Letters, 30(21): 2095
王志娟, 韩力慧, 陈旭锋, 程水源, 李悦, 田川, 谢慧. 2012. 北京典型污染过程PM 2.5 的特性和来源. 安全与环境学报, 12(5): 122–126
Wang Z J, Han L H, Cheng X F, Cheng S Y, Li Y, Tian C and Xie H. 2012. Characteristics and sources of PM 2.5 in typical atmospheric pollution episodes in Beijing.Journal of Safety and Environment, 12(5): 122–126
薛文博, 武卫玲, 付飞, 王金南, 韩宝平, 雷宇. 2015. 中国2013年1月PM 2.5 重污染过程卫星反演研究. 环境科学, 36(3): 794–800
Xue W B, Wu W L, Fu F, Wang J N, Han B P and Lei Y. 2015. Satellite retrieval of a heavy pollution process in January 2013 in China.Environmental Science, 36(3): 794–800
于建华, 虞统, 魏强, 王欣, 时建纲, 李海军. 2004. 北京地区PM 10 和PM 2.5 质量浓度的变化特征. 环境科学研究, 17(1): 45–47
Yu J H, Yu T, Wei Q, Wang X, Shi J G and Li H J. 2004. Characteristics of mass concentration variations of PM 10 and PM 2.5 in Beijing area. Research of Environmental Sciences, 17(1): 45–47
相关作者
相关机构
京公网安备11010802024621
