Elsevier

Environment International

Volume 98, January 2017, Pages 75-81
Environment International

Full Length Article
Spatial and temporal trends in the mortality burden of air pollution in China: 2004–2012

https://doi.org/10.1016/j.envint.2016.10.003Get rights and content

Highlights

  • PM2.5 in China caused huge mortality burdens with increasing trends from 2004 to 2012.

  • In–migration and population growth offset the health benefits of pollution control.

  • Health burdens showed strong spatial variations within China.

  • Adjust priority areas for pollution control to reflect the health-burden hotspots.

  • This study adds useful spatial and temporal dimensions to prior estimates for China.

Abstract

While recent assessments have quantified the burden of air pollution at the national scale in China, air quality managers would benefit from assessments that disaggregate health impacts over regions and over time. We took advantage of a new 10 × 10 km satellite-based PM2.5 dataset to analyze spatial and temporal trends of air pollution health impacts in China, from 2004 to 2012. Results showed that national PM2.5 related deaths from stroke, ischemic heart disease and lung cancer increased from approximately 800,000 cases in 2004 to over 1.2 million cases in 2012. The health burden exhibited strong spatial variations, with high attributable deaths concentrated in regions including the Beijing–Tianjin Metropolitan Region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, Shandong, Wuhan Metropolitan Region, Changsha–Zhuzhou–Xiangtan, Henan, and Anhui, which have heavy air pollution, high population density, or both. Increasing trends were found in most provinces, but with varied growth rates. While there was some evidence for improving air quality in recent years, this was offset somewhat by the countervailing influences of in–migration together with population growth. We recommend that priority areas for future national air pollution control policies be adjusted to better reflect the spatial hotspots of health burdens. Satellite-based exposure and health impact assessments can be a useful tool for tracking progress on both air quality and population health burden reductions.

Introduction

Over recent decades, rapid economic development has led to worsening air quality in China (Wang et al., 2014, Wu et al., 2012, Zhao et al., 2013a, Zhao et al., 2013b), which now ranks as one of the most polluted countries in the world (Van Donkelaar et al., 2010, Verstraeten et al., 2015). As a leading modifiable risk factor for non–communicable diseases, air pollution has attracted considerable interest from both the research and policy communities (Krewski et al., 2009, Laden et al., 2006, Pope et al., 2009, Pope and Dockery, 2006, Shang et al., 2013, West et al., 2016, Yang et al., 2013). However, assessments of health impacts, and their trends over time and space, have been hampered by the limited availability of relevant air monitoring prior to 2013 in China. Initial health impact studies used surrogate measures such as ambient PM10 concentrations measured at fixed monitoring stations to estimate health outcomes attributable to China's air pollution (Cheng et al., 2013, Hou et al., 2012, Matus et al., 2012, Zhang et al., 2008). However, PM10 is a less robust health–related exposure metric than PM2.5 (USEPA, 2012), and central–site monitoring data may lead to exposure uncertainty related to spatial variability of PM, population density and demographic characteristics (Steinle et al., 2013).

In recent years, methods have been developed for estimating ground–level PM2.5 concentrations based on satellite derived aerosol optical depth (AOD), providing a promising alternative for estimating exposure to outdoor PM2.5 and associated health impacts, with more extensive spatial and temporal coverage (Brauer et al., 2012, Brauer et al., 2015, Ma et al., 2014, Yao and Lu, 2014). For example, taking advantage of a global PM2.5 dataset derived from AOD, the Global Burden of Disease (GBD) project reported that outdoor air pollution in China caused 1.2 million premature deaths and 25 million disability adjusted life years (DALY) losses in 2010 (Yang et al., 2013). A subsequent study reported a lower mortality burden of 0.35–0.50 million premature deaths, based on a different exposure methodology (Chen et al., 2013a). Both of these PM2.5-based health assessments reported results only for a single year and for China as a whole.

Recently, as the Chinese government's demands for air quality management broadened from understanding the scope of the problem to targeting interventions, identification of spatial and temporal trends in health burdens attributable to air pollution is becoming more and more important. A recent study evaluated the temporal trend and spatial distributions of PM10–related health impacts in China based on monitoring data from 2001 to 2011, contributing valuable insights into this question (Cheng et al., 2013). However, because PM10 trends may not track those of PM2.5, spatial and temporal trends derived from PM10 data can be misleading (Cheng et al., 2013, Ma et al., 2016). Therefore, more accurate and refined information of the spatial–temporal characteristics of PM2.5 effects are needed to support future policy interventions.

To address this need, we used a new 10 km resolution satellite derived PM2.5 dataset in conjunction with fine scale population data to develop novel estimates of PM2.5 related health damage in China from 2004 to 2012 at the subnational scale (Burnett et al., 2014, Ma et al., 2016, Yang et al., 2009). The implications of observed temporal trends and spatial distributions of impacts for future policy directions are explored.

Section snippets

Methods and data

While several air pollutants are known to have adverse health impacts, we focus here on outdoor PM2.5 as the indicator of risk as it is widely regarded as the single best metric of air pollution–related risk to public health (USEPA, 2012). Regarding outcomes, we selected the premature deaths caused by stroke (International Classification of Diseases Revision 10 codes/ICD-10: I60–I69), ischemic heart disease (IHD, ICD-10: I20–I25), and lung cancer (LC, ICD-10: C33–C34) because of strong evidence

Results

During the period 2004–2012, over 93% of people in China lived in areas where PM2.5 exceeded China's National Air Quality Standard for GradeII of 35 μg/m3 (Fig. 1). Population weighted exposure (PWE) averaged between 67.1 and 76.7 μg/m3 for China as a whole (Table S1). To visualize how regional impacts of PM2.5 are distributed across the concentration range, we plot the distribution of population as a function of ambient PM2.5 in 2004 (Fig. 1A) and 2012 (Fig. 1B). The distributions were bimodal,

Discussion

Using a satellite-derived reconstruction of PM2.5 concentrations at 10 km resolution across China from 2004 to 2012, we observed significant variations of PM2.5 related health burdens over time and across provinces. These new results add useful spatial and temporal dimensions to previous national scale, point-in-time health burden estimates for China (Chen et al., 2013a, Yang et al., 2013). There were several important differences in our analysis as compared to the GBD study. Whereas the GBD

Conclusions

This study provides a refined estimation of historical health damage attributable to outdoor air pollution in China at 10 × 10 km resolution from 2004 to 2012. To our knowledge, this is the first study to quantify the health consequences of China's PM2.5 pollution at both a long time scale and sub–national level. Though some limitations exist, this work contributes valuable insights into the strong spatial and temporal variations of air pollution related health burdens in China. The findings of

Role of the funding source

The funder of the study had no role in study design or data collection. ML, JB, PLK and coauthors had full access to all the data in the study. JB and PLK had final responsibility for the decision to submit for publication.

Acknowledgement

The study was supported by Natural Sciences Foundation of China (71433007), Jiangsu Science and Technology Support Program (SBE2014070918), Collaborative Innovation Center for Regional Environmental Quality, and Collaborative Innovation Center for Atmospheric Environment and Equipment. The work of M. Liu was also supported by China Scholarship Council (CSC) under the State Scholarship Fund. The work of P. Kinney was partially supported by NIEHS grant P30ES009089. The work of Y. Liu was

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