Estimation of nighttime PM2.5 concentrations over Seoul using Suomi NPP/ VIIRS Day/Night Band

With rapid economic development and urban growth, Seoul experiences severe air pollution due to fine particulate matter with an aerodynamic diameter of <= 2.5 mu m (PM2.5), 2.5 ), which is detrimental to human health. Although recent studies have extensively focused on estimating daytime PM 2.5 concentrations using various types of satellite data, there remains a significant lack of research on nighttime PM 2.5 estimations. This study estimated nighttime PM 2.5 in Seoul from December 2018 to November 2019 using multiple linear regression (MLR) and random forest (RF) models. These models, which incorporated data on radiance, moon illumination fraction, and terrain height from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi National Polar-orbiting Partnership satellite covering all moon phases, also utilized meteorological data from the ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF). To address multicollinearity, seasonal models were developed using forward stepwise regression and variance inflation factor analysis. DNB radiance analysis indicates that the high intensity of artificial light sources in Seoul significantly reduces the impact of moonlight, leading to notable changes in the DNB radiation associated with PM 2.5 concentrations. Consequently, this study estimated nighttime PM 2.5 over Seoul across all moon phases. These estimates were then validated through 10-fold cross-validation. The RF model exhibited superior accuracy, with a coefficient of determination (R2) 2 ) of 0.65-0.90, compared to MLR, with R2 2 of 0.15-0.50, reflecting seasonal fluctuations in the model performance. The developed models can be applied to estimate reliable nighttime PM 2.5 concentrations in megacities with strong artificial light sources, utilizing a comprehensive dataset from satellite observations for all moon phases. Additionally, our findings can serve as scientific data for establishing environmental policies by providing valuable insights into understanding air pollution primarily caused by PM2.5. 2.5 .