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At present, fine particulate matter (PM_2.5) 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 PM_2.5 air pollution problems, feasible large-scale continuous spatial PM_2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM_2.5 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 PM_2.5 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 R² 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 PM_2.5 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 PM_2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM_2.5 concentration inversion.