Impact of climate change on heat-related mortality in Jiangsu Province, China☆
Graphical abstract
Introduction
Heat exposure has been associated with increases in both total non-accidental mortality and cause-specific mortality from cardiovascular and respiratory diseases (Basu, 2009, Bobb et al., 2014, Curriero et al., 2002, Hajat and Kosatky, 2010, Yang et al., 2013). A warming climate is projected to increase future heat-related total non-accidental mortality across developed countries (Ballester et al., 2011, Guo et al., 2016, Huang et al., 2011, Kingsley et al., 2016, Li et al., 2013, Petkova et al., 2013, Vardoulakis et al., 2014). However, few studies have estimated the impact of climate change on heat-related specific causes of death, such as cardiovascular mortality and respiratory mortality (Li et al., 2015). Anticipating changes in future cause-specific mortality is crucial to understanding and reducing future population vulnerability to climate change. In addition, many of these studies have focused on urban areas due to the urban heat island effect and high density of susceptible population (Huang et al., 2011, Li et al., 2013). However, there is emerging evidence supporting high risk of heat-related health impacts in nonurban areas (Bennett et al., 2014, Chen et al., 2016, Madrigano et al., 2015, Sarofim et al., 2016, Sheridan and Dolney, 2003). Less is known about how total and cause-specific mortality will change in response to changes in projected heat exposure in nonurban areas. Moreover, limited studies have specifically assessed climate change impacts on heat-related mortality in developing countries such as China where socioeconomic and demographic conditions differ from those in developed counties. To date in China, only limited evidence of climate change impacts on heat-related mortality were found in Beijing, China, leading to considerable uncertainty as to whether the single city result can be applied to larger regions of China (Li et al., 2015).
Projecting heat-related mortality under a changing climate requires information on the exposure response function (ERF) for temperature-related mortality, projected changes in temperature, baseline rates of cause-specific mortality, and the size of the exposed population (Huang et al., 2011), all of which contribute uncertainties. The choice of ERF contributes a large part of the variations in estimating future temperature-related mortality (Benmarhnia et al., 2014, Wu et al., 2014). As the ERF of heat-related mortality can vary substantially within countries (Bennett et al., 2014, Ma et al., 2015), a region-specific ERF instead of a single ERF covering different regions or countries is critically important in evaluating the impact of climate change on regional heat-related mortality. Another source of uncertainty lies in the projected temperature, based on both scenarios of future ‘forcing’ associated with greenhouse gas concentrations and from climate model response to those greenhouse gas concentrations. The latter varies due to different model formulations, representation of processes and initial states (Flato et al., 2013), so a multi-model ensemble approach is required to address this uncertainty (Li et al., 2013). In addition, population growth would also affect the impact of climate change on heat-related health effects by increasing exposed population, which has not been well considered in many previous studies (Jones et al., 2015).
In this study, we aimed to assess the impact of climate change on heat-related total and cause-specific mortality in both urban and rural counties of Jiangsu Province, China. We applied urban-specific and nonurban-specific ERFs for heat-related total, cardiovascular (including more specific causes of stroke and ischemic heart disease (IHD)), and respiratory (including chronic obstructive pulmonary disease (COPD)) mortality from our previous analysis (Chen et al., 2016) to multiple climate and population projections to estimate the climate change-induced heat-related health burdens in 104 counties of Jiangsu Province, China.
Section snippets
Methods
This study was conducted in 104 counties of Jiangsu Province, China with a total population of 78.2 million people in 2010. Jiangsu Province is located along the eastern coast of China and is the most densely populated province in China. Situated in the transition belt from a subtropical to temperate zone, Jiangsu Province has a typical monsoonal climate with an average daily mean temperature of 15.7 °C and four distinct seasons. Jiangsu Province is one of the most developed regions in China
Results
Fig. 1a shows the multi-model simulated time series of annual warm season (May to September) mean daily average temperature during the historical and future periods. Compared to the historical period 1980–2005, projected ambient temperatures will continually increase in 2016–2070 under both the RCP4.5 and RCP8.5 scenarios. In general, RCP8.5 yielded higher daily average temperatures than RCP4.5, and this difference becomes larger over time. Under RCP4.5, the projected 21-GCMs mean daily average
Discussion
Under both RCP scenarios, projected warmer temperatures in the 2016–2040 and 2041–2065 periods will lead to higher heat-related mortality for total non-accidental, cardiovascular, respiratory, stroke, IHD, and COPD causes occurring annually during May to September in Jiangsu Province, China. Nonurban residents in Jiangsu will suffer from more excess heat-related cause-specific mortality in 2016–2065 than urban residents. Climate models and scenarios dominate the estimation uncertainty of future
Conclusion
In summary, we found that warming temperatures projected for 2016–2040 and 2041–2065 could lead to higher heat-related mortality in Jiangsu Province, China. Nonurban residents will suffer from more heat-related death burden than urban residents in the future five decades. The results argue for targeted climate change mitigation and adaptation measures tailored to both urban and nonurban areas of Jiangsu Province. Specific public health interventions could be focused on the leading causes of
Conflict of interest
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the China National Natural Science Foundation (grant number: 71433007), the China National Key Research & Development Program (grant number: 2016YFC0207600), the US National Institute of Environmental Health Sciences (grant numbers: ES009089 and T32ES023770), the Shanghai Tongji Gao Tingyao Environmental Science & Technology Development Foundation (STGEF). Climate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames
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This paper has been recommended for acceptance by David Carpenter.