*2.2. Data*

The annual PM2.5 concentrations from 2000 to 2015 in the panel data were extracted from the global PM2.5 concentration with a spatial resolution of 0.01◦ (http://fizz.phys.dal.ca/~{}atmos/martin/ ?page\_id=140#V4.CH.02) [13,26–28]. The global PM2.5 concentration data set was implemented by the atmospheric chemistry driven model GEOS-Chem. The algorithm in the model combines the aerosol optical depth obtained from multi-sensor products with the data from surface monitoring stations [13,29,30]. The correlation coefficient of the estimated and regulatory monitored PM2.5 concentration was 0.81 [28]. To avoid uncertainty in the subsequent analysis caused by abnormal or missing values in the data, the three-year average was used as an annual average. The average PM2.5 concentrations from 2000 to 2015 in 14 prefecture-level cities were extracted and calculated by city boundaries (Figure 2).

Referring to relevant studies, we selected GDP per capita (GDPPC), the proportion of urban impervious surface area (UIS) and the value added by industry as a percentage of GDP (IND) to represent the economic growth, urbanization and industrialization of each city, respectively [22]. The panel data on the economic growth and industrialization of the fourteen prefecture-level cities in Liaoning Province from 2000 to 2015 were collected from the China City Statistical Yearbook. Because China has cancelled the agricultural and non-agricultural household registration system since 2014, to avoid abnormal fluctuation of time series data, the proportion of urban artificial impervious surface area rather than the traditional proportion of urban population was used to express the urbanization level of each city [31]. The spatial resolutions of 30 m urban artificial impervious area data were obtained from Fine Resolution Observation and Monitoring of Global Land Cover (FROM-GLC, http://data.ess.tsinghua.edu.cn/urbanChina.html) [31,32]. The GDPPC data were converted to constant prices, and all data were logarithmically transformed to stabilize the time series data and reduce the heteroscedasticity when performing empirical tests (*ln*PM2.5, *ln*GDPPC, *ln*UIS and *ln*IND).

**Figure 2.** Spatial distribution of surface PM2.5 concentrations in Liaoning province from 2000 to 2015.
