## 2 Materials and Methods
### Study Data
#### 2.2.2 Remote Sensing, Meteorological, and Socioeconomic Data  
We selected the 2010, 2015, and 2020 Landsat land cover and population density data to map the spatial-temporal characteristics in the \"2 + 26\" cities of BTH. The urban construction land data were obtained from the 30 m resolution China Land Cover Dataset from the Resource and Environmental Data of the Chinese Academy of Sciences (www.resdc.cn; accessed on 17 September 2023). The population density data were obtained from the 1000 m \(\times\) 1000 m resolution LandScan population density dataset ([[https://landscan.ornl.gov/](https://landscan.ornl.gov/)]([https://landscan.ornl.gov/](https://landscan.ornl.gov/)); accessed on 17 September 2023).  
Considering the socioeconomic causes of air pollutants [41], the population density (P (person/km\({}^{2}\))), gross domestic product (GDP), industry structure (Ind (%)), and bus ownership (Bus (units)) were selected as the socioeconomic data, which were obtained from the 2011, 2016, and 2021 China Urban Statistical Yearbook.  
Unfavorable meteorological conditions are detrimental to the dispersion of atmospheric pollutants [42; 43]. Therefore, the meteorological data of temperature (T (\({}^{\circ}\)C)), precipitation (PCP (mm)), relative humidity (RH (%)), and wind speed (WS (m/s)) were picked as the dependent variables. These data were obtained from the hour-by-hour National Meteorological Science Data Center database from 2010 to 2020 ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)); accessed on 17 September 2023). In this study, Table 1 shows that China's land use remote sensing data, China's socio-economic data, air pollutants, and meteorological datasets are used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Name** & **Abbreviated Variable and Unit** & **Data Format** & **Data Source** \\ \hline \multirow{3}{*}{population density} & \multirow{3}{*}{P/(Per person/km\({}^{2}\))} & \multirow{3}{*}{Raster/1 km} & LandScan population density dataset \\  & & & ([[https://landscan.ornl.gov/](https://landscan.ornl.gov/)]([https://landscan.ornl.gov/](https://landscan.ornl.gov/)); accessed on 17 September 2023) \\ \hline \multirow{3}{*}{Land use} & \multirow{3}{*}{/} & \multirow{3}{*}{Raster/30 m} & Resource and Environment Science and Data \\  & & & Center, Chinese Academy of Sciences \\  & & & (www.resdc.cn; accessed on 17 September 2023) \\ \hline \multirow{3}{*}{the gross domestic product} & GDP/(Yuan/Per person) & Excel & The China Statistical Yearbook 2011, 2016, and \\  & & & 2020 ([[https://www.stats.gov.cn/s/ndsi/](https://www.stats.gov.cn/s/ndsi/)]([https://www.stats.gov.cn/s/ndsi/](https://www.stats.gov.cn/s/ndsi/)); accessed on 17 September 2023) \\ \hline \multirow{3}{*}{the industry structure} & Ind/(\%) & Excel & 2020 ([[https://www.stats.gov.cn/s/ndsi/](https://www.stats.gov.cn/s/ndsi/)]([https://www.stats.gov.cn/s/ndsi/](https://www.stats.gov.cn/s/ndsi/)); accessed on 17 September 2023) \\ \cline{1-1}  & bus ownership & Bus/(units) & Excel & \\ \hline \multirow{3}{*}{Temperature} & T/(\({}^{\circ}\)C) & Excel & \\ \cline{1-1}  & Precipitation & PCP/(mm) & Excel & The National Meteorological Science Data Center \\ \cline{1-1}  & Wind speed & WS/(m/s) & Excel & \\ \cline{1-1}  & Relative humidity & RH/(\%) & Excel & \\ \hline \multirow{3}{*}{PM\({}_{2.5}\) pollutants} & \multirow{3}{*}{PM\({}_{2.5}\) pollutants} & \multirow{3}{*}{PM\({}_{2.5}\)/(\(\upg/\text{m}^{3}\))} & \multirow{3}{*}{Excel} & The National Urban Air Quality Real-time \\  & & & Distribution Platform \\ \cline{1-1}  & & & ([[https://air.cnemc.cn:18007/](https://air.cnemc.cn:18007/)]([https://air.cnemc.cn:18007/](https://air.cnemc.cn:18007/)); accessed on 17 September 2023) \\ \hline \hline \end{tabular} Note: We use acronyms for variables to define them: P–population density; GDP–gross domestic product; Ind–industry structure; Bus–bus ownership; T–temperature; PCP–precipitation; RH–relative humidity; WS–windspeed.  
\end{table}
Table 1: Data sources.