Features of extreme pollution and its influencing factors: evidence from China

Extreme $$ pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme $$ pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level $$ concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that $$ concentration, $$ concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of $$ concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of $$ concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of $$ and $$ concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies.