期刊
REMOTE SENSING
卷 13, 期 17, 页码 -出版社
MDPI
DOI: 10.3390/rs13173405
关键词
nighttime light image; light radiance at night; nighttime PM2; 5 concentration; Pearl River Delta urban agglomeration
类别
资金
- National Natural Science Foundation of China [41971423, 31972951, 41771462]
- Natural Science Foundation of Hunan Province [2020JJ3020]
- Science and Technology Program of Hunan Province [2019RS2043, 2019GK2132]
- Outstanding Youth Project of the Education Department of Hunan Province [18B224]
This study establishes a correlation model between nighttime light radiance and ground PM2.5 concentration, and proposes a multiple linear regression model to estimate PM2.5 concentrations in the Pearl River Delta region. Results show a high estimation accuracy with a notable temporal and spatial heterogeneity of model accuracy. This research provides practical value and potential in nighttime ground PM2.5 concentration inversion.
At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R-2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion.
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