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Spatial–Temporal Analysis of Air Pollution, Climate Change, and Total Mortality in 120 Cities of China, 2012–2013

Overview of attention for article published in Frontiers in Public Health, July 2016
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Title
Spatial–Temporal Analysis of Air Pollution, Climate Change, and Total Mortality in 120 Cities of China, 2012–2013
Published in
Frontiers in Public Health, July 2016
DOI 10.3389/fpubh.2016.00143
Pubmed ID
Authors

Longjian Liu, Xuan Yang, Hui Liu, Mingquan Wang, Seth Welles, Shannon Márquez, Arthur Frank, Charles N. Haas

Abstract

China has had a rapid increase in its economy over the past three decades. However, the economic boom came at a certain cost of depleting air quality. In the study, we aimed to examine the burden of air pollution and its association with climatic factors and health outcomes using data from Chinese national and city-level air quality and public health surveillance systems. City-level daily air pollution index (API, a sum weighted index of SO2, NO2, PM10, CO, and Ozone) in 120 cities in 2012 and 2013, and its association with climate factors were analyzed using multiple linear regression analysis, spatial autocorrelation analysis, and panel fixed models. City-level ecological association between annual average API and total mortality were examined using univariate and partial correlation analysis. Sensitivity analysis was conducted by taking the consideration of time-lag effect between exposures and outcomes. The results show that among the 120 cities, annual average API significantly increased from 2012 to 2013 (65.05 vs. 75.99, p < 0.0001). The highest average API was in winter, and the lowest in summer. A significantly spatial clustering of elevated API was observed, with the highest API in northwest China in 2012 and with the highest in east China in 2013. In 2012, 5 (4%) of the 120 cities had ≥60 days with API >100 (defined as "slightly polluted"), however, it increased to 21 cities (18%) that experienced API >100 for ≥60 days in 2013. Furthermore, 16 cities (13%) in 2012 and 35 (29%) in 2013 experienced a maximum API >300 (defined as "severely polluted"). API was negatively and significantly correlated with heat index, precipitation, and sunshine hours, but positively with air pressure. Cities with higher API concentrations had significantly higher total mortality rates than those with lower API. About a 4-7% of the variation in total mortality could be explained by the difference in API across the nation. In conclusion, the study highlights an increased trend of air pollution from 2012 to 2013 in China. The magnitude of air pollution varied by seasons and regions and correlated with climatic factors and total mortality across the country.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Student > Master 7 13%
Researcher 7 13%
Professor > Associate Professor 3 6%
Student > Doctoral Student 2 4%
Other 7 13%
Unknown 14 26%
Readers by discipline Count As %
Environmental Science 12 23%
Medicine and Dentistry 4 8%
Social Sciences 4 8%
Computer Science 3 6%
Engineering 3 6%
Other 11 21%
Unknown 16 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 July 2016.
All research outputs
#21,300,766
of 23,926,844 outputs
Outputs from Frontiers in Public Health
#8,439
of 11,604 outputs
Outputs of similar age
#325,304
of 368,183 outputs
Outputs of similar age from Frontiers in Public Health
#74
of 77 outputs
Altmetric has tracked 23,926,844 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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