This paper constructs a vector autoregressive (VAR) model and vector error correction (VECM) model, analyzes the air pollution, economic development, and national health of China from 1990 to 2019, and evaluates the economic losses from the respiratory diseases caused by air pollution. The results show that: (1) China’s economy continues to grow, and the corresponding amount of exhaust gas emissions (during the study period) showed a trend of first increasing and then slowly decreasing. (2) The overall burden of respiratory diseases in China showed a downward trend, with significant differences in gender and age. (3) A significant long-term equilibrium relationship existed between per capita gross domestic product (PGDP), exhaust emissions, and the disability-adjusted life years (DALYs) of the respiratory disease burden. Exhaust emissions will bring about short-term fluctuations of PGDP and disease burden DALYs. Air pollution is mainly caused by exhaust gas emissions, and DALYs and PGDP have little effect on air pollution. (4) Indirect economic losses from respiratory diseases caused by air pollution are likely to be long-term and will impose increasing pressure. On the basis of the healthy and sustainable operation of the economic system, the government should effectively prevent environmental health risks and improve the pollution treatment level.

This paper mainly focuses on the relationship between the subjective evaluation of air quality and the quality of life (QOL) of middle-aged and elderly residents in China. The 2018 China Health and Retirement Longitudinal Study (CHARLS) project database is the key sources of data, from which 16,736 valid samples were used in our research. Multivariate linear regression analysis and binomial logistic regression model were applied to detect the impact of the subjective evaluation of air quality on QOL, which was evaluated in two dimensions, which are health utility and experienced utility, using the health utility EQ-5D score and the experienced utility of life satisfaction score. Our results show that there is a significant positive correlation between the subjective evaluation of air quality and the two dimensions of QOL. Age, education, marital status and sleep status also have a relatively great impact on the QOL of residents. This worked studied the overall QOL of middle-aged and elderly residents in China, while policy suggestions regarding high-quality air public goods are also given in the paper.

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 cities is low in the southeast and high in the northwest, and low in the coastal areas and higher in the inland areas. The correlation coefficients between AQI and the pollutant concentrations are as follows: fine particulate matter (PM2.5), inhalable particulate matter (PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) values are correlated at 0.89, 0.84, 0.54, 0.54, 0.32, and 0.056, respectively. (3) In terms of short-term AQI predictions, the RF model performs better than the SARIMA model. The long-term forecast indicates that the average AQI value in Chinese cities is expected to decrease by 0.32 points in 2032 compared to the 2022 level of 52.95. This study has some guiding significance for the analysis and prediction of urban air quality.