Driving Factors of Monthly PM2.5 Concentrations in Shanghai (2020–2024): Panel Data Estimation

Abstract

Ambient fine particulate matter (PM2.5) continues to pose serious risks to public health, yet the interaction between weather conditions and human-induced emissions in coastal megacities has not been fully captured by existing studies. To fill this gap, the present work develops an integrated econometric framework for quantifying the main determinants of Shanghai's PM2.5 levels over the 2020–2024 period. A balanced monthly panel of 60 observations was used; both fixed-effects and random-effects regressions were applied, and the Hausman test was used to select the model, while OLS results were also compared for consistency; the results showed that weather factors like wind speed and rainfall are the main short-run drivers; specifically, a 1 m/s increase in wind speed reduces PM2.5 by 2.45 μg/m³, and a 1 mm rise in rainfall leads to a 0.05 μg/m³ decline; as for human-caused sources, the number of vehicles and industrial production stand out as the main positive contributors; an increase of 10,000 vehicles is associated with a 1.2 μg/m³ increase in PM2.5; on the other hand, when urban green coverage expands by 1%, PM2.5 goes down by 0.85 μg/m³; the Hausman test produced a p-value of 0.03, which means the fixed-effects model is chosen, and this points to the need to account for unobserved factors that stay constant over time; these findings indicate that nature and human actions are both crucial, and the study lends empirical backing to policies such as promoting cleaner industrial processes, regulating traffic, and expanding urban green spaces.