Retrieval of Atmospheric Visibility and Its Driving Factors in Shanghai, China

The combined effects of meteorological factors and aerosol chemical compositions on atmospheric visibility in Shanghai were investigated in this study based on the observed hourly dataset during 2022–2024. Correlation analysis and random forest modeling are employed to quantify the relative contributions of these factors. The results reveal significant negative correlations between visibility and both PM2.5 concentration and relative humidity, with partial correlation coefficient of −0.62 and −0.61. Nitrate, ammonium, and other aerosol components substantially modulate these relationships. The random forest model explains 83% of the variance when only meteorological variables are considered, increasing to 93% with the inclusion of aerosol chemical composition. Under 30 km high-visibility conditions, PM2.5 is the dominant predictor (39%) of atmospheric visibility variation, followed by relative humidity (35%). In contrast, during low-visibility conditions (lower than 7.5 km), relative humidity becomes the primary contributor (30%), the influence of PM2.5 weakens (18%), and aerosol chemical components account for a larger share (30%). These findings provide important insights into the mechanisms governing visibility variability under different environmental conditions.