Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China.

ABSTRACT: To describe the spatiotemporal variations characteristics and future trends of urban air quality in China, this study evaluates the spatiotemporal evolution features and linkages between the air quality index (AQI) and six primary pollution indicators, using air quality monitoring data from 2014 to 2022. Seasonal autoregressive integrated moving average (SARIMA) and random forest (RF) models are created to forecast air quality. (1) The study's findings indicate that pollution levels and air quality index values in Chinese cities decline annually, following a "U"-shaped pattern with a monthly variation. The pollutant levels are high in winter and low in spring, and low in summer and rising in the fall (O₃ shows the opposite). (2) The spatial distribution of air quality in Chinese

SUBMITTER: Du Y 

PROVIDER: S-EPMC10235078 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature