Inversion algorithm of PM<sub>2.5</sub> air quality based on nighttime light data from NPP-VIIRS

Xiaoran ZHAO; Hanqing SHI; Pinglyu YANG; Lei ZHANG; Xun FANG; Kuai LIANG

doi:10.11834/jrs.20176162

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Inversion algorithm of PM_2.5 air quality based on nighttime light data from NPP-VIIRS
Vol. 21, Issue 2, Pages: 291-299(2017)
Published： 2017-3 ，
DOI： 10.11834/jrs.20176162

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Xiaoran ZHAO, Hanqing SHI, Pinglyu YANG, et al. Inversion algorithm of PM_2.5 air quality based on nighttime light data from NPP-VIIRS. [J]. Journal of Remote sensing 21(2):291-299(2017)
DOI：

Xiaoran ZHAO, Hanqing SHI, Pinglyu YANG, et al. Inversion algorithm of PM_2.5 air quality based on nighttime light data from NPP-VIIRS. [J]. Journal of Remote sensing 21(2):291-299(2017) DOI： 10.11834/jrs.20176162.

摘要

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。

Abstract

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

and ln(

) 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

=0.98

–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.

关键词

微光夜间PM2.5VIIRS/DNB支持向量机

Keywords

low lightnighttime PM2.5VIIRS/DNBsupport vector machine

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Inversion algorithm of PM2.5 air quality based on nighttime light data from NPP-VIIRS

DOI： 10.11834/jrs.20176162

摘要

Abstract

关键词

Keywords

references

Inversion algorithm of PM_2.5 air quality based on nighttime light data from NPP-VIIRS