Transition in air pollution, disease burden and health cost in China: A comparative study of long-term and short-term exposure☆
Graphical abstract
Introduction
Ambient air pollution is one of the leading risk factors to human health (GBD 2017 Risk Factor Collaborators, 2018), causing substantial economic cost to the society (World Bank, 2016; Yin et al., 2020). Globally, long-term exposure to air pollution contributed to 4.2 million (Cohen et al., 2017) to 8.9 million deaths (Burnett et al., 2018). Among the various air pollutants, fine particulate matter with aerodynamic diameter equal to or smaller than 2.5 μm (PM2.5) and tropospheric ozone (O3) are the major contributors to the health impacts (Pope et al., 2002; Jerrett et al., 2009), which represent as a proxy indicator of ambient air pollution in global burden of disease (GBD) projects (Lim et al., 2012; GBD 2017 Risk Factor Collaborators, 2018).
Driven by rapid economic development and urbanization, China has suffered from air pollution problem for decades (Xing et al., 2015; Liu et al., 2020; Lu et al., 2020). In 2013, severe pollution episodes attacked frequently over large areas of the country (Andersson et al., 2015). To improve air quality, the State Council issued the Air Pollution Prevention and Control Action Plan in 2013, which for the first time announced to reduce annual average PM2.5 concentrations by up to 25% by 2017 (State Council of the People’s Republic of China, 2013). Studies showed that nationwide population-weighted annual PM2.5 concentration dropped significantly since then (Zheng et al., 2018; Zhang et al., 2019) which averted 0.2 million premature deaths (Xue et al., 2019).
In contrast, clean air actions was not able to stop the increasing trend of ozone pollution (K. Li et al., 2019b). During 2013–2019, the trend of summer maximum 8-h average (MDA8) ozone was 1.9 ppb a−1 across China and 3.3 ppb a−1 in the North China Plain (NCP) (Li et al., 2020). The contrasting trend of the two major air pollutants in recent years led to a transition of PM2.5-dominating air pollution to PM2.5- and O3-dominating condition. Therefore, an overall assessment of the public health burden and economic cost associated with PM2.5 and O3 is needed to facilitate the adjustment of clean air policies.
In addition to the species of air pollutants, the exposure duration of air pollution also significantly influences its health damage. Short-term exposure, which varies on a daily or hourly basis, mainly introduces acute effects (Liu et al., 2019). In contrast, the long-term exposure of air pollution is correlated with chronic health damages estimated from cohort studies (Laden et al., 2006; Cai et al., 2016). The distinction in short-term and long-term health effects associated with air pollution could provide detail information for pollution prevention.
The literature has well documented the health impacts associated with long-term exposure of PM2.5 in China on national or regional scales while the health burden related to short-term exposure has been under explored (Xie et al., 2019; Luo et al., 2020). Most studies on the health burden associated with short-term exposure did not capture spatial variations and coverages in air pollution concentrations or explored only one species of air pollutants. For example, Yao et al. (2020) applied the monitoring data in 338 Chinese cities to estimate the health burden attributable to short-term exposure of multiple air pollutants (including PM2.5 and O3), but did not consider the spatial variations of air pollution within cities, and did not include estimates in the rural areas. In contrast, the studies on the PM2.5-related short-term health impact have better spatial coverages. Xue et al. (2019) applied an optimal estimator of daily PM2.5 by combining observations, satellite measurements, and simulations to evaluate the health benefits from PM2.5 reduction during 2013–2017, but the effects of O3 were not considered.
Health cost associated with air pollution has been recognized as key information input in cost-benefit assessments that facilitate science-based policy in air pollution control. To quantify the health costs, an age-invariant value of statistical life (VSL) or value of statistical life year (VSLY) was widely used to estimate the cost of health risks (World Bank, 2016; J. Li et al., 2019a; Yang et al., 2019). The health cost estimated by VSL/VSLY method reflects not only direct cost (e.g. medical cost) and indirect cost (e.g. productivity loss), but also intangible cost due to painfulness of life loss that was not captured in the cost of illness method. On the other hand, some empirical and theoretical studies argued that both VSL and VSLY vary with people’s age due to relatively shorter life expectancy, life quality and wealth conditions (Alberini et al., 2004; Aldy and Viscusi, 2008; Hammitt, 2013). To capture the effects of health status, life expectancy and wealth condition on VSL, this study adopted age-adjusted VSL valuation method developed by Yin et al. (2020) to estimate the age-specific health cost of air pollution.
So far, there have been plenty of studies evaluating the PM2.5 air quality improvements and health benefits from the clean air actions during 2013–2017 (Zheng et al., 2017; Ding et al., 2019; Xue et al., 2019; Zhang et al., 2019; Yue et al., 2020), but a study comprehensively depicting the broad picture of the health cost attributable to both long-term and short-term exposure of PM2.5 and O3 in China is still not available. To fill such gap in the literature, this study aims to explore both annual and daily patterns of exposure, health burden and economic cost of major air pollutants (PM2.5 and O3) from 2013 to 2018. Leveraging the methodology developed in our previous work (Liu et al., 2016; Yin et al., 2020), we evaluate the premature deaths and their costs attributable to both long-term and short-term exposure of PM2.5 and O3 during 2013–2018 with newly established dataset of spatial and temporal resolved ground-level PM2.5 and O3 concentrations (Kong et al., 2020). The results of this study provide an overall view of the transition in China’s health burden and economic cost associated with air pollution and provided the region-specific policy strategies in the co-control of these two air pollutants.
Section snippets
PM2.5 and O3 exposure estimates
We obtained the hourly PM2.5 and O3 concentrations in China during 2013–2018 from the chemical data assimilation system developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (Available at: https://doi.org/10.11922/sciencedb.00053, accessed on January 10, 2021). Details on the methodology were published elsewhere (Tang et al., 2011; Kong et al., 2020). In short, the three-dimensional chemical transport model named Nested Air Quality Prediction Modeling System (NAQPMS) (
Changes in long-term and short-term exposure of PM2.5 and O3
Fig. 1 shows the spatial distribution of annual average PM2.5 concentration and the long-term (6-month period with highest mean) MDA8 O3 concentration across China in 2013 and 2018, and the concentration differences between the study period. From 2013 to 2018, high levels of PM2.5 concentrations appeared in northern and Central China, especially in the BTH and surrounding regions (Fig. 1A and B), where the emission and population density are both very high. Significant reductions of PM2.5
Significance of this study
In this study, we investigated both long-term and short-term mortality effects and economic costs associated with exposure to air pollution in China, by utilizing the improved spatial and temporal resolved numeric estimates of ground PM2.5 and O3 concentrations from a data assimilation system, the up-to-date exposure-response functions and economic valuation measure. Compared with previous studies, we contributed to the literature in three aspects. First, we presented a broad view of the
Conclusions
This study provided a broad view of the transitions in long-term and short-term PM2.5 and O3 concentration, as well as corresponding health burden and health cost from 2013 to 2018. The long-term exposure to PM2.5 and O3 was associated with an estimate of 2.26 million (1.91, 2.60) premature deaths in 2018. In comparison, the premature deaths attributable to short-term exposure to PM2.5 and O3 in 2018 were 96% lower than the health burden due to long-term exposure. In addition, we found
Credit roles
Jun Liu, Conceptualization, Methodology, Investigation, Data curation, Writing, original draft preparation. Hao Yin, Methodology, Investigation, Data curation, Writing, original draft preparation. Xiao Tang, Methodology, Investigation, Data curation, Reviewing and editing. Tong Zhu, Conceptualization, Reviewing and editing. Qiang Zhang, Supervision, Methodology, Reviewing and editing. Zhu Liu, Supervision, Methodology, Reviewing and editing. Xiaolong Tang, Conceptualization, Supervision,
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
J.L. acknowledges the funding from Beijing Natural Science Foundation (grant No. 8192024), the China Postdoctoral Science Foundation (grant No. 2018M641382) and Fundamental Research Funds for the Central Universities (grant No. 06500166); H.Y. acknowledges funding from the National Natural Science Foundation of China (grant No. 71904104) and the China Postdoctoral Science Foundation (grant No. 2019M650726); X.T. acknowledges funding from the National Natural Science Foundation of China (grant
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This paper has been recommended for acceptance by Pavlos Kassomenos.
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Jun Liu and Hao Yin contributed equally to this paper.