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, meteor-ology 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 association remained significant after adjustment for socioeconomic status. In conclusion, the study highlights the burden and seasonal, regional and areas variations in air pollution across the nation. Air pollution is estimated to account for more than 4% of the urban health inequality in total mortality in China.
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 [
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.
In China, daily air pollution data are reported by the Chinese Ministry of Environmental Protection (MEP) [
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 [
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.
Data on annual average city-level mortality from all-cause in 2014 were collected from China Health Statistics and Statistical Communique of the People’s Republic of China on National Economic and Social Development [
Number of total population and gross domestic product (GDP) in 2014 by cities were obtained from 2014 National Economic and Social Development Statistics Bulletin [
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 regions. Seasons were classified as spring (March to May), summer (June to August), fall (September to November), and winter (December to February). Six regions are grouped: North China (Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia), Northeast China (Liaoning, Jilin, and Heilongjiang), East China (Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi and Shandong), South Central China (Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Hong Kong, and Macau), Southwest China (Chongqing, Sichuan, Guizhou, Yunnan, and Tibet), and Northwest China (Shannxi, Gansu, Qinghai, Ningxia, and Xinjiang) [
In the second group of analyses, spatial patterns of AQI and air pollutants were described using Arc Geographic Information System (Version 12, Redlands, CA) [
I ( h ) = n N ( h ) ∑ i ∑ j w i j ( Z ( s i ) − Z ( s ) ¯ ) ( Z ( s j ) − Z ( s ) ¯ ) ∑ i ( Z ( s i ) − Z ( s ) ¯ ) 2
where wij 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 [
C ( h ) = n − 1 2 N ( h ) ∑ i ∑ j w i j ( Z ( s i ) − Z ( s j ) ) 2 ∑ i ( Z ( s i ) − Z ( s ) ¯ ) 2
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 [
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) [
No. Citiesb | Mean | (SD) | Median | IQRa | ||
---|---|---|---|---|---|---|
25% | 75% | |||||
Air quality indicator | ||||||
Air quality index (AQI) | 161 | 95.69 | (37.21) | 89.28 | 70.62 | 112.31 |
Climate indicators | ||||||
Temperature (˚C) | 50 | 14.34 | (11.00) | 16.35 | 7.29 | 23.20 |
Heat index | 50 | 53.27 | (21.71) | 57.31 | 39.37 | 70.86 |
Precipitation (0.1 mm) | 50 | 758.15 | (949.37) | 439.00 | 94.00 | 1079.00 |
Sunshine hours (hrs) | 50 | 172.35 | (69.54) | 177.65 | 120.15 | 221.90 |
Pressure (kPa) | 50 | 97.82 | (5.33) | 99.91 | 97.35 | 100.87 |
a: IQR: Interquartile range; b: Of 161 cities with AQI measures, 50 cities had daily climate change measures.
(±SD) of AQI were in winter (119.58 ± 2.19), followed by spring (95.02 ± 1.35), fall (89.30 ± 1.37), and summer (78.86 ± 1.05) in 2014 (
ANOVA test | Rank test | ||||||
---|---|---|---|---|---|---|---|
No. cities | Mean | (SE) | p-value | Median | p-value | ||
Season | |||||||
Spring (Mar, Apr, May) | 161 | 95.02 | (1.35) | <.0001 | 91.67 | <.0001 | |
Summer (Jun, Jul, Aug) | 161 | 78.86 | (1.05) | ref | 75.93 | ref | |
Fall (Sep, Oct, Nov) | 161 | 89.30 | (1.37) | <.0001 | 84.16 | 0.003 | |
Winter ( Dec, Jan, Feb) | 161 | 119.58 | (2.19) | <.0001 | 108.57 | <.0001 | |
Region | |||||||
North | 22 | 117.36 | (2.73) | <.0001 | 110.00 | <.0001 | |
Northeast | 16 | 90.06 | (1.89) | 0.021 | 87.16 | <.0001 | |
East | 50 | 99.23 | (1.26) | <.0001 | 93.71 | <.0001 | |
South central | 43 | 85.18 | (1.66) | 0.238 | 77.46 | 0.436 | |
Southwest | 15 | 81.54 | (2.72) | ref | 71.02 | ref | |
Northwest | 15 | 102.39 | (2.79) | <.0001 | 97.05 | <.0001 |
*AN0VA: Analysis of Variance. Rank test used Mann-Whitney U test.
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.
In 2014, Of 161 cities, more than one third (37.27% - 39.75%) had AQI 100-149, and 67 cities (41.61%) had AQI 150 and higher in January (
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 (
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Excellent | ||||||||||||
No. cities | 0 | 4 | 3 | 3 | 14 | 12 | 18 | 18 | 14 | 0 | 3 | 1 |
% | 0 | 2.48 | 1.86 | 1.86 | 8.7 | 7.45 | 11.18 | 11.18 | 8.7 | 0 | 1.86 | 0.62 |
Good | ||||||||||||
No. cities | 34 | 70 | 86 | 110 | 83 | 106 | 117 | 125 | 134 | 98 | 94 | 85 |
% | 21.12 | 43.48 | 53.42 | 68.32 | 51.55 | 65.84 | 72.67 | 77.64 | 83.23 | 60.87 | 58.39 | 52.8 |
Light polluted | ||||||||||||
No. cities | 60 | 61 | 62 | 43 | 63 | 43 | 26 | 18 | 13 | 54 | 52 | 64 |
% | 37.27 | 37.89 | 38.51 | 26.71 | 39.13 | 26.71 | 16.15 | 11.18 | 8.07 | 33.54 | 32.3 | 39.75 |
Moderately polluted | ||||||||||||
No. cities | 67 | 26 | 10 | 5 | 1 | 0 | 0 | 0 | 0 | 9 | 12 | 11 |
% | 41.61 | 16.15 | 6.21 | 3.11 | 0.62 | 0 | 0 | 0 | 0 | 5.59 | 7.45 | 6.83 |
a: Air quality level: Excellent: AQI < 50; Good: AQI ≥ 50 and AQO < 100; Light polluted: AQI ≥ 100 and AQI < 150; Moderately polluted: AQI ≥ 150.
Similar correlation relationships between QAI and meteorological factors were observed using panel pooled analysis. It indicates that 28.8% (R2) of the variations in AQI could be explained by the differences in meteorological factors (
Number of cities | Pearson | Spearman | |||
---|---|---|---|---|---|
r | p-value | r | p-value | ||
Temperature (˚C) | 50 | −0.44 | <0.0001 | −0.50 | <0.0001 |
Heat index | 50 | −0.44 | <0.0001 | −0.50 | <0.0001 |
Precipitation (0.1 mm) | 50 | −0.39 | <0.0001 | −0.47 | <0.0001 |
Sunshine hours (hrs) | 50 | −0.16 | 0.000 | −0.11 | 0.009 |
Pressure (kPa) | 50 | 0.13 | 0.001 | 0.18 | <0.0001 |
Pooled Model | |||||||||
---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | ||||||
Betaa | p | Beta | p | Beta | p | Beta | p | ||
Year 2014 | |||||||||
Sample size (n) | |||||||||
Heat Index | −0.720 | <0.0001 | −0.534 | <0.0001 | −0.434 | <.0001 | -0.485 | <.0001 | |
Precipitation (0.1 mm) | −0.008 | <0.0001 | −0.011 | <0.0001 | -0.011 | <.0001 | |||
Sunshine hours (hrs) | −0.115 | <0.0001 | -0.086 | <.0001 | |||||
Pressure (kPa) | 0.926 | 0.0007 | |||||||
Fixed time effect (Month) | N | N | N | N | |||||
R-Squareb | 0.196 | 0.231 | 0.274 | 0.288 | |||||
One-way fixed-month effect model | |||||||||
Sample size (n) | |||||||||
Heat Index | −0.303 | 0.004 | −0.198 | 0.060 | −0.370 | 0.001 | −0.516 | <0.0001 | |
Precipitation (0.1 mm) | −0.007 | <0.0001 | −0.012 | <0.0001 | −0.012 | <0.0001 | |||
Sunshine hours (hrs) | −0.138 | <0.0001 | −0.123 | <0.0001 | |||||
Pressure (kPa) | 0.856 | 0.002 | |||||||
Fixed time effect (Month) | Y | Y | Y | Y | |||||
R-Square | 0.313 | 0.340 | 0.382 | 0.392 |
a: Beta: Regression coefficient. B: R-Square: Coefficient of determination.
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, R2 = 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, R2=4.4%, p = 0.036).
The main findings of the study highlight that (1) the highest average AQI was in winter, and the lowest in summer in 2014. Significant clustering distributions of cities with high AQI was observed by regions, with the highest AQI in North China, and lowest in Southwest China. (2) Changes in AQI concentrations were significantly correlated with changes in climate factors. (3) A positive and significant association between AQI and total mortality was observed.
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 polluted) in seven months of 2014 (Jan-March, May and Oct.-Dec.), and significantly higher proportions of cities had AQI ≥ 150 (classified as moderately polluted) in Jan (41.61%), Feb (16.15%), Nov (7.45%), and Dec (6.83), respectively. The variations in AQI were significantly and spatially clustered across the country. Overall, North and Northwest China had higher AQI than the rest of the country. Although several individual studies examined the differences in air quality across the cities, the present analysis is one of the first to use a larger sample size (161 cities) to test the geographic differences in AQI across the county. In Huang and his colleagues’ study, they examined air pollution (assessed by PM2.5) from 13 air-monitoring stations in the city in 2013-2014 in Xi’an City, Shannxi province of China, and observed a significant clustering distribution of neighborhoods with elevated PM2.5 concentration across the 13 air-monitoring stations [
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 [
The association between air pollution and public health has been examined in several Chinese cities. These studies have found a positive association of air pollution with years of potential life lost, mortality, cardiovascular disease and respiratory diseases mortalities [
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 [
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.
The Drexel-SARI Co-Research and Education on Low Carbon and Healthy City Technology (LCHCT) Study is support by a joint grant (#282582) of Drexel University Office of International Programs and Shanghai Advanced Research Institute (SARI) of the Chinese Academy of Science. The Drexel-SARI LCHCL Study has provided numerous opportunities of research and training to our under- and graduate students for their practicum, research thesis and research practice in summer internships (2014 and 2016), and as well as to our collaborators and visiting scholars from China. Our specific thanks go to those who assisted data collection and preliminary data analysis in the past years: Drexel SPH MPH2014: Xuehui Qian, Purva Jian; SPH MS2015: Hui Liu, Xuan Yang, Jessica Dow, Ruby Sung; SPH MPH2015: Lili Lin, Xuan Yang, Xiaochen Zhang, and Mingfei Zhao. Kathleen Ross, Emily Valencia; SPH MS2016: Haoyang Guo; SPH MPH: Jinggaofu Shi; Drexel SPH MPH2017: Bingqing Zhang, Siyu Li; Drexel SBESHS MS2017: Chiranjiv Chevli, and Drexel COAS BS2017: Feng Jia and Baihui Huang. Our Specific thanks also go to Professor Julie Mostov, the former Senior Vice-Provost for Global Initiatives at Drexel University (now, the Dean of New York University for Liberal Studies), and to Miss Heidi West, the former Director of Drexel Office of International Programs (now, the Director of Programs and Operations, Global Environmental Health Lab, NY), and Mr. Adam Zahn, the Associate Director of Drexel Office of International Programs for their excellent and continued support for the study.
None.
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