Supplementary data include meteorological data, social-economic data, land use data and mortality information.
3.2.3.1$Meteorological$Data$
In this project, meteorological data was obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis datasets (ERA-Interim). ECMWF uses its data assimilation systems and forecast models to re-analyze observation datasets. As one of the ECMWF’s reanalysis datasets, ERA- Interim is a global atmosphere reanalysis from 1979. In this study, the meteorological data from September 2014 to August 2015 was used. The meteorological data included surface temperature (temperature at 2 m) (K), surface pressure (Pa), wind speed (m/s), relative humidity (%) and boundary layer height (BLH) (m). The acquired time, spatial resolution and step’s information of every factor have been listed in Table 3.2.
Table 3.2 Meteorological data used in this study
Meteorological Data Beijing Time Spatial Resolution Step Temperature (K)
14:00
0.125°
0 Surface Pressure (Pa)
U Wind/V Wind Speed (m/s) Relative humidity (%)
3 Boundary Layer Height (m) 8:00; 20:00
U wind: wind from the west V wind: wind from the south.
Step= 3: this factor is re-analyzed on a 3 hours-averaged time level.
In Table 3.2, wind speeds consist of wind speeds from two directions: Uwind is the wind from west to east while Vwind is the wind from south to north. The temporal resolutions of temperature, wind speed and RH are four times per day (2:00, 8:00, 14:00, and 20:00 Beijing Time). Thus for these three factors, the data acquired at 14:00 was adopted to be matched with the remote sensing AOD time. For BLH, the temporal resolution is twice a day (8:00 and 20:00 Beijing time). Thus BLH at 8:00 and 20:00 were both used in model building because the time variation of BLH is complex during the day time and the BLH at 14:00 cannot be simply determined by BLH at 8:00 and 12:00. The spatial resolutions of these four factors are all of 0.125°. The step indicates whether the data is re-analyzed based on an averaged-time or not. Step 0 means this data is not on an averaged-time level while 3 means this factor is re-analyzed at a 3 hours –
averaged level. The spatially averaged values of these seven factors during research period are shown in Figure 3.5.
The values of annual averaged BLH at 8:00 ranged from 536 m to 1490 m (Figure 3.5 (a)) while those of BLH at 20:00 ranged from 114 m to 394 m (Figure 3.5 (b)): the high values for both factors were mainly distributed on mountain areas. The Uwind and Vwind speed ranged from -0.72 to 3.78 m/s and - 1.46 to 0.63 m/s, respectively. The negative value means the wind was from the inverse direction. From the Figures 3.5 (c) and (d), it can be seen that the plain area of the BTH region, such as Handan, Hengshui and Tianjin, were often experienced the wind from the south while for the mountainous region, such as Chengde and Zhangjiakou, winds were often from the northwest. For the annual averaged RH, though distribution was complex, the coastal regions, such as some areas of Tangshan and Tianjin, had the highest value of RH. The temperature and surface pressure had similar distributions: the low values were located in the plain area while high values were located in mountainous regions.
3.2.3.2$Socio)economic$and$Land$Use$Data$
In this thesis, spatial socio-economic and land used data involves GDP, population, the percent of urban area, and elevation data.
The GDP, population, and land use data were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). Based on 2010 national census data, these two data sets were all generated at 1 km spatial resolution. GDP and population’ spatial distribution are shown in Figures 3.6(a) and (b).
Figure 3.6 GDP and land use data in 2010
RESDC also provided 2010 land use data at 1 km through remote sensing data and other techniques. Basing on the land use data, urban area (Code number: 51) could be extracted and the percent of the urban area was then aggregated on 3 km and 10 km resolutions by ArcGIS 10.3.1 in this study for later analysis. The percent of urban area with 3 km resolution is shown in Figure 3.6 (c). Beijing was the most concentrated city for all three factors.
Elevation data was obtained from the digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) with a resolution of 30m. The elevation data of the BTH region is presented in Figure 3.1.
3.2.3.3 Health Data
averaged standard (15 µg/m3) in the BTH region. Thus health data was also employed in this thesis. Health data involved in this study included an ER and a total non-accidental mortality baseline data. Cao et al. (2011) examined the association between air pollutants and mortality from 1991 to 2000 based on the annual average air pollution exposure in 30 provinces in China. In their research, it is estimated that 10 µg/m3 increase of PM2.5 was corresponded with 0.90% increment in total non-accidental mortality, which was adopted as the ER in this study.
The all-cause mortality of China can be required from China Statistical Yearbook and 2014 is the most recent year with available data. So in this paper, the mortality data of 2014 was used instead of the study period (between September 2014 and Auguest 2015). In 2014, the mortality of China was 0.716% (China Statistical Yearbook, 2015). All-cause mortality consists of non-accidental mortality and accidental mortality. Non-accidental mortality can be categorized by the International Classification of Diseases 10th Revision (ICD-10: A00-R99) (Ma et al., 2015). The detailed causes of death information was provided by the Data-Center of China Public Health Science (DCCPHS) and the most recent version was 2008 National Disease and Cause Death Database. Basing on this database, non-accidental death accounted for 90.44% of all-cause mortality. Due to the lack of more recent data, it was assumed that this proportion kept stable from 2008 to 2014 and 90.44% is then used to determine the 2014 non-accidental mortality from all-cause mortality. Therefore, 2014 non-accidental mortality was estimated to be 0.647%, which was used as the non-accidental mortality baseline in this research.