Climate Change, Air Quality and Urban Health: Evidence from Urban Air Quality Surveillance System in 161 Cities of China 2014

Air pollution has posed a serious public health issue in China. In the study, we aimed to examine the burden of air pollution and its association with climate factors and total mortality. City-level daily air quality index (AQI) data in 161 cities of China in 2014, and meteorological factors, socioeconomic status and total morality were obtained from China environmental, meteorology and healthcare agencies. Linear regression, spatial autocorrelation analysis and panel fixed models were applied in data analysis. Among 161 cities, monthly average AQI was significantly different by seasons and regions. The highest average AQI was in winter, and the lowest in summer. A significant clustering distribution of AQI by cities was observed, with the highest AQI in north China (22 cities, mean = 117.36). Among the 161 cities, 5 cities (3%) had AQI > 150 (e.g., moderate polluted reference value), and 50 cities (31.1%) had AQI between 100 and 150 (slightly polluted value). Daily heat index, precipitation and sunshine hours were negatively and significantly, but air pressure was positively correlated with AQI. Cities with higher AQI concentrations had higher total mortality than those with lower AQI. This AQI-mortality associaHow to cite this paper: Liu, L.J., Yang, X., Wang, M.Q., Long, Y., Shen, H.Q., Nie, Y., Chen, L.X., Guo, H.Y., Jia, F., Nelson, J., Song, G.Z., Frank, A., Welles, S. and Haas, C.N. (2018) Climate Change, Air Quality and Urban Health: Evidence from Urban Air Quality Surveillance System in 161 Cities of China 2014. Journal of Geoscience and Environment Protection, 6, 117-130. https://doi.org/10.4236/gep.2018.63011 Received: March 20, 2018 Accepted: April 5, 2018 Published: April 8, 2018


Introduction
Urban air pollution is becoming a global concern as it has great impacts on the environment and public health. With the rapid increase in urbanization and economy in China since the early 1980s [1], there has been a concomitant cost of worsening air quality. Outdoor air pollution has become a top environmental concern in China [2] [3] [4] [5], and has been examined by several studies [2] [3] [6]- [12]. However, few studies examined the association between climate change and air quality, and how this differs by physical and socioeconomic environments in China. In this study, we applied the most recently nationally representative data from multiple resources on air quality, meteorological factors and health outcomes (e.g. total mortality) that were collected and reported under the guidelines of the Ministry of Environmental Protection, China Meteorological Data Sharing Service System, and National Health Statistics Center of the People's Republic of China [13] [14] [15] [16]. We tested two hypotheses: 1) There are significant variations in air pollution concentrations by seasonal, geographic and socioeconomic regions in China. 2) Air pollution was positively correlated with total mortality rates.

Design
Data (n = 161 cities) from almost half of the total cities (n = 342) in China were analyzed ecologically and cross-sectionally using standard and robust analysis approaches.

Air Pollution Data
In China, daily air pollution data are reported by the Chinese Ministry of Environmental Protection (MEP) [13]. Data on Air Pollution Index (AQI) in 2014 were analyzed for 161 major cities across the country. AQI is a sum indicator calculated from a group of air pollutants, which is derived from daily measurements of five atmospheric pollutants: particulate matter (PM10 and PM2.5), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and ozone (O 3 ). The average daily concentrations of pollutants in individual cities were computed using the centering method [17].

Meteorology Data
To analyze the correlation between daily AQI and daily climate change of 161 cities with daily AQI measures, we were able to collect and match 50 cities that had daily meteorology data available from the Chinese Meteorological Data Sharing Service System [14] [18]. In the present analysis, we included six major meteorological indicators: temperature, relative humidity, precipitation, sunshine hours, pressure and heat index (HI, estimated using the regression equation proposed by Lans Rothfusz [19]). HI = −42.379+2.04901523*T + 10.14333127*RH − 0.22475541*T*RH − 0.00683783*T*T − 0.05481717*RH*RH + 0.00122874*T*T*RH + 0.00085282*T*RH*RH − 0.00000199*T*T*RH*RH where T is temperature in degrees F and RH is relative humidity in percent.

Statistical Analysis
In the first group of analyses, univariate analyses were used to describe the pattern of daily, weekly, seasonally and yearly AQI concentrations by city and re- In the second group of analyses, spatial patterns of AQI and air pollutants were described using Arc Geographic Information System (Version 12, Redlands, CA) [21] and the correlation of AQI with heat index and spatial variations were examined using linear correlation and spatial autocorrelation analysis techniques. In Spatial autocorrelation, Moran's I statistics were calculated using For locations s and s + h, the variance of the difference of the Z(s) and Z(s + h) is of interest. Moran's I is calculated based on cross-products of the deviations from the mean： where w ij are the elements of the weight matrix, and N(h) is the sum of the elements of the weight matrix.
Meanwhile, Geary's C statistic was calculated, an alternative method to measure spatial autocorrelation, estimated using the following formula [22].
In the third group of analyses, correlations between AQI and climate factors were examined using linear correlation and regression analysis. Because of the nature of the spatial-based cross-sectional and time-series data, we further applied Panel (data) analysis methods [23] [24]. We performed an independently pooled model, and a fixed effect model, because time-specific effects on AQI are not randomly distributed in the data.
In the fourth group of analyses, the association between AQI and total mortality were examined using simple and partial (adjusted) correlation analysis methods. In the partial correlation analysis, we adjusted for city-level socioeconomic status (assessed by gross domestic product per capital).
All statistical analyses were conducted using SAS software, version 9.3 (SAS Institute, Cary, North Carolina) [22]. The level of significance was set at a 2-sided test at p ≤ 0.05.     Table 2). The ANOVA test shows that compared with summer as the baseline, there were significant difference between spring and summer (p < 0.0001), fall and summer (p<0.0001), winter and summer (p < 0.0001).   Figure   3 depicts the annual AQI concentrations across the nation (on the right-side).

Spatial Patterns of AQI
Spatial autocorrelation analysis technique was further applied to test whether cities with higher AQI were clustered in certain areas. The result indicates that Journal of Geoscience and Environment Protection  Moran's I coefficient (0.22) was greater than 0 (p < 0.0001), and Geary's C coefficient (0.80) was less than 1 (p < 0.0001). Both statistics indicate that the values of AQI measures across cities had positive spatial autocorrelation, meaning the spatial distribution of high AQI values were more spatially clustered rather than dispersed.

Correlation between AQI and Meteorological Factors
Of the 161 cities with AQI measures, 50 cities had available data for meteorological measures. Among these cities, simple correlation analysis indicated that monthly average AQI concentrations were significantly and negatively correlated with temperature, heat index, precipitation, and sunshine hours, but significantly and positively associated with pressure (Table 4).

Ecological Association between Annual Average AQI and Total Mortality
Of 161 cities with AQI data, we are able to collect reliable total mortality data from 110 cities in 2014. Annual average AQI concentrations were positively and significantly correlated with total mortality (r = 0.2, R 2 = 4.0%, p = 0.034). After adjustment for socioeconomic status (assessed by gross domestic product), the association between AQI and total mortality in 2014 remained statistically significant (r = 0.21, R 2 =4.4%, p = 0.036).

Discussion
The main findings of the study highlight that (1)

Burden of Air Pollution in China
China is facing a great challenge of controlling air pollution, especially in urban cities and certain regions because of their great differences in geo-physical and socioeconomic environmental status. Our study shows that about more than one third (32% -39%) of the study cities had AQI ≥ 100 (classified as slightly pol- They used air quality data monitored from 2000 to 2005. Their findings suggested that the average air quality in Guangzhou over 6 years was higher than that in Beijing, Tianjin, Nanjing, Hangzhou and Shanghai, and lower than that in Shenzhen, Zhuhai and Shantou [26]. Findings of our present study added new evidence by using a much larger sample size and applying more vigorous analysis to the body of the research literature and highlighted a serious variation in the burden of air pollution across the country.

Climate Change and AQI
Climate change can be caused in part by increased atmospheric concentrations of carbon dioxide and other green-house gases. It is likely to result in changes in temperature, humidity, amount, distribution, and intensity of precipitation events and the intensity and frequency of certain extreme weather events [27]. In our study, significant and negative associations of AQI with heat index, precipitation, and positive association with atmospheric pressure were observed. A higher concentration of AQI in winter and lower concentration in summer has also been reported by other studies in China

Impact of Air Pollution on Population Health
The association between air pollution and public health has been examined in several Chinese cities. These studies have found a positive association of air pol- Chinese cities [24]. Our study has an advantage that utilized data from multiple sources released by the Chinese national air quality and health surveillance systems and was able to test for and confirm a significant and positive association between AQI and all-cause mortality. This finding further addresses the serious impact of air quality on urban public health.
There are several limitations that should be kept in mind when interpreting the major findings of this study. First, the associations between AQI and climate factors were analyzed cross-sectionally. It is not necessary to interpret as a cause-effect association, although changes in regional distribution of temperature show that a warming trend was more significant in West, East and North China than in South China [6]. Second, data for the analysis of the association between AQI and meteorological factors were only available in 50 cities of the 161, which reduced the statistical power when we tested the association between AQI and mortality with adjustment for climate factors (data not shown). Third, more detail health related data at individual city levels were not available.
Therefore, further studies with multiple-sits and multiple disciplinary collaboration are needed. Nevertheless, the present study also has several advantages.
First, as compared to all previous publications, the present study had the largest sample size on air quality which was measured in 161 major cities, almost half of the total cities of China. Second, the accuracy and quality of data on AQI, meteorological and health outcomes are reliable as they were consistent and reported by Chinese authority agencies, which offer a comparable base for further studies. Third, using the spatial autocorrelation technique, the present study is able to quantitatively test and confirm a spatial clustering of AQI across the regions of China. The panel fixed analysis is one of the first to address the collection between AQI and meteorological factors by fixed-months effect (differences in temperature across the regions by months). The study provides further evidence to public health policy makers on air quality control and health outcomes promotion.
In conclusion, findings of this study indicate that the burden of air pollution remains a serious public health issue. AQI significantly varies across the country, and is significantly associated with climate factors, and is positively and significantly associated with total mortality.