The authors have declared that no competing interests exist.
Climate change is likely to further worsen ozone pollution in already heavily polluted areas, leading to increased ozone-related health burdens. However, little evidence exists in China, the world’s largest greenhouse gas emitter and most populated country. As China is embracing an aging population with changing population size and falling age-standardized mortality rates, the potential impact of population change on ozone-related health burdens is unclear. Moreover, little is known about the seasonal variation of ozone-related health burdens under climate change. We aimed to assess near-term (mid-21st century) future annual and seasonal excess mortality from short-term exposure to ambient ozone in 104 Chinese cities under 2 climate and emission change scenarios and 6 population change scenarios.
We collected historical ambient ozone observations, population change projections, and baseline mortality rates in 104 cities across China during April 27, 2013, to October 31, 2015 (2013–2015), which included approximately 13% of the total population of mainland China. Using historical ozone monitoring data, we performed bias correction and spatially downscaled future ozone projections at a coarse spatial resolution (2.0° × 2.5°) for the period April 27, 2053, to October 31, 2055 (2053–2055), from a global chemistry–climate model to a fine spatial resolution (0.25° × 0.25°) under 2 Intergovernmental Panel on Climate Change Representative Concentration Pathways (RCPs): RCP4.5, a moderate global warming and emission scenario where global warming is between 1.5°C and 2.0°C, and RCP8.5, a high global warming and emission scenario where global warming exceeds 2.0°C. We then estimated the future annual and seasonal ozone-related acute excess mortality attributable to both climate and population changes using cause-specific, age-group-specific, and season-specific concentration–response functions (CRFs). We used Monte Carlo simulations to obtain empirical confidence intervals (eCIs), quantifying the uncertainty in CRFs and the variability across ensemble members (i.e., 3 predictions of future climate and air quality from slightly different starting conditions) of the global model. Estimates of future changes in annual ozone-related mortality are sensitive to the choice of global warming and emission scenario, decreasing under RCP4.5 (−24.0%) due to declining ozone precursor emissions but increasing under RCP8.5 (10.7%) due to warming climate in 2053–2055 relative to 2013–2015. Higher ambient ozone occurs under the high global warming and emission scenario (RCP8.5), leading to an excess 1,476 (95% eCI: 898 to 2,977) non-accidental deaths per year in 2053–2055 relative to 2013–2015. Future ozone-related acute excess mortality from cardiovascular diseases was 5–8 times greater than that from respiratory diseases. Ozone concentrations increase by 15.1 parts per billion (10−9) in colder months (November to April), contributing to a net yearly increase of 22.3% (95% eCI: 7.7% to 35.4%) in ozone-related mortality under RCP8.5. An aging population, with the proportion of the population aged 65 years and above increased from 8% in 2010 to 24%–33% in 2050, will substantially amplify future ozone-related mortality, leading to a net increase of 23,838 to 78,560 deaths (110% to 363%). Our analysis was mainly limited by using a single global chemistry–climate model and the statistical downscaling approach to project ozone changes under climate change.
Our analysis shows increased future ozone-related acute excess mortality under the high global warming and emission scenario RCP8.5 for an aging population in China. Comparison with the lower global warming and emission scenario RCP4.5 suggests that climate change mitigation measures are needed to prevent a rising health burden from exposure to ambient ozone pollution in China.
Patrick Kinney and colleagues model seasonal and annual future ozone-associated mortality in 104 Chinese cities with different population sizes
Climate change is likely to increase ozone-attributed health burdens, which are not well understood in China, the world’s largest greenhouse gas emitter, with a rapidly aging population.
Few published studies have jointly evaluated future ozone-related mortality considering changes in climate, population size, population aging, and age-group-specific mortality rates.
Prior research has mainly investigated summertime ozone, but little is known about the potential seasonal variations of ozone-related acute excess mortality under climate change.
We used a global chemistry–climate model and recently available ambient ozone monitoring data to show future changes in ozone-related acute excess mortality from 2013–2015 to 2053–2055 under different climate and population change scenarios in 104 cities across China.
We show that global climate change, particularly rising methane, leads to a 10.7% increase in annual ozone-related non-accidental deaths under a high global warming and emission scenario but a 24.0% decrease under a lower global warming and emission scenario.
Although population size is expected to decline and age-group-specific mortality rates are expected to decrease, population aging offsets these decreases and yields 1–4 times greater ozone-related mortality under climate change in 2053–2055 than that in 2013–2015.
Ozone concentration increases in the cold season (November to April) under a high global warming and emission scenario, leading to a net increase of ozone-related mortality in 2053–2055.
To our knowledge, this is the first study to quantify the amplifying effect of population aging on ozone-related mortality under alternative scenarios of climate and population change while accounting for decreases in population size and age-group-specific mortality rates.
Cold season ozone may play a more important role than currently recognized in future ozone-related mortality burden attributable to climate change, and future studies on seasonal patterns are warranted.
This study suggests that the ozone-related mortality burden in China will rise under a high global warming and emission scenario of climate change, and mitigation measures are needed to reduce the associated health burden.
Climate change threatens human health via multiple pathways, including both direct impacts from changes in temperature, precipitation, and frequency of extreme weather and indirect impacts mediated through both natural systems (e.g., air quality and disease vectors) and human systems (e.g., undernutrition and mental stress) [
Future ozone-related health burdens will reflect both the impact of changing ozone concentrations and changes in the size and age distribution of the exposed population. Previous studies on ozone-related health impacts under climate change have been limited by only considering changes in population size [
Little is known about the seasonal variation of ozone-related health burdens under climate change. Most previous studies only projected ozone-related acute excess mortality in the warm season, resulting in an incomplete understanding of the ozone-related acute excess mortality across the year [
In this study, we aimed to estimate near-term future (mid-21st century) changes in annual and seasonal ozone-related acute excess mortality in China attributable to climate and emission change and population change, based on ozone projections from a global chemistry–climate model statistically downscaled to a fine spatial resolution (0.25° × 0.25°) using recently available ambient ozone monitoring data from across China. We estimated the near-term future changes because of their importance to decision makers in government and industry for acting on mitigation and adaptation to reduce the health impacts of climate change. We used 3 ensemble members from the coupled chemistry–climate model in each of 2 climate and emission change scenarios and 6 population scenarios to account for potential uncertainties in climate models, greenhouse gas and pollution emission projections, and population change models [
This analysis was conducted in 104 Chinese cities (
(A) Spatial distribution of annual average daily ozone concentration (parts per billion [ppb]) during 2013–2015 (historical period) in 104 Chinese cities. Ozone concentration is the maximum daily 8-hour average. (B) Historical (2013–2015) and projected (2053–2055) annual average daily ozone concentration (ppb) in 104 Chinese cities under the RCP4.5 and RCP8.5 scenarios. RCP4.5 and RCP8.5 represent moderate and high global warming and emission scenarios, respectively. The horizontal line within each box represents the median concentration among 104 cities, the lower and upper boundaries of the box indicate the 25th and 75th percentiles, and the ends of the whisker lines indicate the maximum and minimum concentrations within 1.5 times the interquartile range from the upper and lower box boundaries. (C) Projected population size in 104 Chinese cities from 2010 to 2050 under 6 population change scenarios under different shared socioeconomic pathways (SSPs). (D) Projected population aging in China from 2010 to 2050 under 5 SSP population change scenarios.
Ambient ozone observations were obtained from the China National Environmental Monitoring Center (
We analyzed global ozone simulations performed with the Geophysical Fluid Dynamics Laboratory chemistry–climate model CM3 (GFDL-CM3), which were conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5), in support of the Intergovernmental Panel on Climate Change Fifth Assessment Report [
The Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5 were used to evaluate the future global change, from both well-mixed greenhouse gases and emissions of air pollutants and their precursors, in ozone concentrations, representing moderate and high global warming and emission scenarios, respectively [
Compared with observations, daily GFDL-CM3 simulations overestimated ozone concentrations in the historical period by an average of +17 ppb (36%) across ensemble members. We used a bias-correction spatial disaggregation (BCSD) method to downscale the GFDL-CM3 simulations to a high spatial resolution of 0.25° × 0.25° (approximately 25 km × 25 km). Briefly, in the bias-correction step, we first compared the daily GFDL-CM3 simulations with corresponding daily ozone observations within each coarse-scale grid cell (2.0° × 2.5°) and each month to identify the monthly bias at each quantile in the historical simulations relative to observations. We matched the historical simulations and observations within the same month rather than on the same day to obtain the historical monthly biases. We then corrected model estimates in the future periods, assuming that historical monthly biases persist in the future. We further downscaled the bias-corrected projections to the high spatial resolution (0.25° × 0.25°) using a spatial disaggregation method. In short, we spatially translated the bias-corrected future model simulations from the coarse scale (2.0° × 2.5°) to the fine scale (0.25° × 0.25°). Using the spatial distribution of monthly mean ozone observations as a spatial guide, we first subtracted the coarse-scale spatial distribution of observations from coarse-scale bias-corrected projections to get temporal scaling factors. Then we added the fine-scale spatial distribution of observations back to the fine-scale temporal scaling factors that were interpolated using a bilinear interpolation method. See
To explore the sensitivity of ozone-related acute excess mortality estimates to assumptions about population change, 6 population scenarios were applied in this analysis: a no population change scenario assuming that the Chinese population remains the same from 2010 to 2050, and 5 population projections under the shared socioeconomic pathways (SSPs) in China. For the former, city-level population data were collected from the 2010 Population Census of China. The latter 5 scenarios are drawn from the SSPs, which describe a set of plausible alternative futures of societal development without considering the effects of climate change and new climate policies over the 21st century [
Chinese population size projections between 2010 and 2050 for all ages under the 5 SSPs at 0.125° × 0.125° resolution were extracted from the global projections [
Because China has higher air pollution levels and may also differ in terms of age structure, population sensitivity to air pollution, and components of air pollution mixture compared to developed countries [
Mortality | CRF |
Number of deaths | |||
---|---|---|---|---|---|
Mean ± SD | Minimum | Median | Maximum | ||
All non-accidental causes | 0.24 (0.13, 0.35) | 23 ± 23 | 3 | 18 | 165 |
Cardiovascular | 0.27 (0.10, 0.44) | 11 ± 10 | 1 | 9 | 65 |
Respiratory | 0.18 (−0.11, 0.47) | 3 ± 4 | 0 | 2 | 34 |
5–64 years | 0.13 (−0.23, 0.48) | 5 ± 5 | 1 | 4 | 43 |
65–74 years | 0.19 (0.03, 0.34) | 5 ± 5 | 1 | 4 | 37 |
≥75 years | 0.42 (0.21, 0.64) | 13 ± 13 | 1 | 9 | 84 |
Warm (May–Oct) | 0.20 (0.08, 0.31) | 21 ± 21 | 3 | 15 | 155 |
Cold (Nov–Apr) | 0.43 (0.21, 0.65) | 20 ± 17 | 3 | 16 | 118 |
aCRFs are expressed as the percentage increase (95% confidence interval) in daily mortality associated with a 10-μg/m3 (approximately 5 parts per billion) increase of maximum daily 8-hour average ozone exposure on the current day and previous 3 days. CRFs were obtained from a previous nationwide time-series study in 272 Chinese cities [
City-level baseline cause-specific, age-group-specific, and seasonal mortality counts were also obtained from the previous publication [
We used the attributable fraction (AF) method to estimate the daily ozone-related acute excess mortality in 2013–2015 and 2053–2055, respectively. The AF is the fraction of baseline mortality attributable to ozone exposure, which is defined as [
We calculated the cause-specific, age-group-specific, and season-specific ozone-related acute excess mortality separately by applying corresponding CRFs and baseline mortality. We computed the GFDL-CM3 ensemble-averaged total excess mortality in 104 cities by combinations of RCPs and population scenarios. We used Monte Carlo simulations to quantify the uncertainty in our mortality estimates by incorporating uncertainty from coefficients of CRFs and model variability across 3 ensemble members of GFDL-CM3. We obtained the empirical confidence intervals (eCIs) from the empirical distribution across 1,000 coefficient samples and 3 ensemble members, assuming normal distributions for the estimated cause-specific, age-group-specific, and season-specific coefficients, respectively. When estimating future ozone-related acute excess mortality under population aging scenarios, we further incorporated the uncertainty from age-group-specific mortality rate changes in the Monte Carlo simulations by assuming normal distributions for the age-group-specific changes in total mortality rate separately. We did not include uncertainty associated with population projections in mortality estimates, because these are not reported in SSP population projections and our goal was scenario-based projections for understanding the implications of health consequences under future demographic and climatic conditions. To evaluate the influence of the fine-scale ozone projections on health impact assessments, we conducted a sensitivity analysis by directly using the coarse-scale bias-corrected ozone projections to estimate future ozone-related cause-specific excess mortality under a no population change scenario.
Four factors—ozone concentration changes due to climate and emission change, population size changes, population aging, and age-group-specific mortality rate changes—contribute to the total future ozone-related acute excess mortality. To decompose changes in ozone-related acute excess mortality, we estimated the contribution from each factor incrementally for the population aged 5 years and above with age-group-specific risk estimates. We calculated the effect due to climate and emission change by applying the no population change scenario, the effect due to population size by subtracting the climate and emission change effect from the effect when applying the population size changes under the 5 SSPs, the effect due to population aging by subtracting the effect when applying the population size changes from the effect when applying the population changes in both size and age structure under the 5 SSPs, and the effect due to age-group-specific mortality rate by subtracting the effect when applying population changes in size and age structure from the effect when applying both population changes in size and age structure and age-group-specific mortality rate changes.
During 2013–2015, the mean annual average of observed ozone concentrations in 104 Chinese cities was 41.6 ppb, with higher ozone concentrations observed in cities located along the east coast of China (
Under the SSPs, population size in 104 Chinese cities would first increase from 2010 to 2030, then decrease from 2030 to 2050 (
In the historical period, short-term exposure to ambient ozone contributed to a total of 13,856 non-accidental deaths annually in 104 Chinese cities. Historical ozone-related acute excess mortality varied substantially among cities, with the smallest attributable deaths in Haikou (15) and the largest attributable deaths in Nantong (820) (
(A) Spatial distribution of historical annual ozone-related mortality in 104 Chinese cities during 2013–2015. (B) Spatial distribution of future changes (%) in annual ozone-related mortality under the RCP4.5 scenario in 104 Chinese cities during 2053–2055 relative to the historical period 2013–2015. (C) Same as (B) but under RCP8.5. (D) Future changes (%) in ozone-related mortality by cause of death (cardiovascular, respiratory, and other causes of non-accidental deaths) under RCP4.5 and RCP8.5. RCP4.5 and RCP8.5 represent moderate and high global warming and emission scenarios, respectively.
Population | Population scenario | Baseline mortality rate | Cause of death | Mean change in mortality (95% empirical CI) by climate and emission change scenario | |
---|---|---|---|---|---|
RCP4.5 | RCP8.5 | ||||
All ages |
No change | No change | All non-accidental causes | −3,332 (−5,877, −2,191) | 1,476 (898, 2,977) |
Cardiovascular | −1,687 (−3,370, −776) | 888 (404, 1,925) | |||
Respiratory | −357 (−1,138, 308) | 101 (−75, 380) | |||
Population aged 5 years and above |
SSP1 | Age-group-specific mortality changes | All non-accidental causes | 47,804 (20,602, 80,463) | 78,560 (35,611, 131,541) |
SSP2 | All non-accidental causes | 35,004 (14,606, 60,544) | 60,140 (26,299, 99,484) | ||
SSP3 | All non-accidental causes | 23,838 (8,609, 43,114) | 44,063 (18,530, 73,377) | ||
SSP4 | All non-accidental causes | 36,357 (15,368, 63,214) | 62,049 (27,497, 10,2124) | ||
SSP5 | All non-accidental causes | 47,765 (19,933, 80,981) | 78,505 (34,083, 129,084) |
aFor the population including all ages, the calculation was based on the risk estimate of ozone-related mortality and annual baseline mortality for the whole population.
bFor the population aged 5 years and above, the calculation was based on the risk estimates of ozone-related mortality and annual baseline mortality for the population age groups 5–64, 65–74, and 75+ years.
Using the coarse-scale (2.0° × 2.5°) ozone projections yielded larger ozone concentration changes in the future period, with an average of +1.3 ppb under RCP4.5 and +1.4 ppb under RCP8.5 in 104 Chinese cities (
Using age-group-specific CRFs and baseline mortality yielded a larger ozone-attributed death burden (21,592) than using all-age CRFs and baseline mortality (13,856) in the historical period. Higher estimates of annual ozone-related acute excess mortality were observed when the population changed in both size and age structure under the 5 SSPs (
The evolving patterns of future ozone-related acute excess mortality reflected the influence not only of climate and emission change, but also of changes in population size, aging, and age-group-specific mortality rates.
Population changes include both population size changes and population aging. Mortality rate indicates age-group-specific baseline mortality rate changes. Future changes (%) of annual ozone-related mortality for the population aged 5 years and above in 2053–2055 were calculated relative to the historical period 2013–2015. RCP4.5 and RCP8.5 represent moderate and high global warming and emission scenarios, respectively. SSP1–5 represent 5 population change scenarios under different shared socioeconomic pathways.
Seasonal ozone changes projected under both RCPs are reported in
(A) Future changes in annual average daily ozone concentrations (parts per billion [ppb]) during the whole year, the warm season (May–October), and the cold season (November–April) in 2053–2055 relative to 2013–2015. RCP4.5 and RCP8.5 represent moderate and high global warming and emission scenarios, respectively. The horizontal line within each box represents the median change in ozone concentration among 104 cities, the lower and upper boundaries of the box indicate the 25th and 75th percentiles, and the ends of the whisker lines indicate the maximum and minimum concentrations within 1.5 times the interquartile range from the upper and lower box boundaries. (B) Future changes (%) in annual ozone-related mortality during the warm season and the cold season in 2053–2055 relative to 2013–2015. Population size scenarios include a no change scenario and 5 population change scenarios under shared socioeconomic pathways (SSPs). Vertical lines denote 95% empirical CIs of net change.
In this study, we projected future ozone-related acute excess mortality in 104 Chinese cities using statistically downscaled ozone projections at a fine spatial resolution (0.25° × 0.25°). Future changes in ozone concentration and related non-accidental deaths from 2013–2015 to 2053–2055 exhibited different patterns in China under RCP4.5 and RCP8.5. Due to the impact of global climate and emission change, an increase in ozone-related deaths was estimated under RCP8.5, whereas a decrease was noted under RCP4.5. Cardiovascular mortality accounted for more than half of the mortality impacts from global climate and emission change. Population aging will likely amplify the ozone-related acute excess mortality burden under both RCPs. Furthermore, under RCP8.5, increased ozone concentrations (+15.1 ppb) in colder months (November to April) will lead to more ozone-related deaths (28.1%) than those avoided (−5.9%) due to decreased ozone concentrations (−4.2 ppb) in warmer months. Thus, deteriorating ozone air quality in colder months will make a growing contribution to the adverse health impacts of climate change in China.
Our estimates for projected changes in annual average ozone concentration in 104 Chinese cities from 2013–2015 to 2053–2055 (−9.3 ppb under RCP4.5 and +5.3 ppb under RCP8.5) are broadly consistent with recent modeling studies based on the latest RCP scenarios, which reported a decreasing ozone concentration for RCP4.5 (around −7 to −6 ppb) and an increasing concentration for RCP8.5 (0–3 ppb) in China over 2000–2050 [
Few studies have assessed the impacts of climate change on ozone-related cause-specific mortality, especially mortality from cardiovascular diseases. Constrained by available CRFs, previous studies focused on either respiratory mortality from long-term exposure [
Future ozone-related acute excess mortality will be substantially amplified by an aging population in the 104 Chinese cities studied (
The seasonal variations in ozone-related acute excess mortality burdens reflect changes in seasonal ozone concentrations (
Few studies have evaluated the seasonal variations of acute ozone-related health impacts under climate change [
Our results are based on a recent reference time period (2013–2015), which was chosen due to the availability of national measurements of ambient ozone concentrations. Compared with earlier time periods (e.g., 2000–2002), this reference time period has higher levels of ozone pollution [
To the best of our knowledge, this study is the first to account for population aging through age-group-specific risk estimates and for declining baseline age-group-specific mortality rates to understand future ozone-related mortality under alternative scenarios of climate and population change. This is also one of the first studies to apply season-specific risk estimates and baseline mortality rates to assess future mortality from changes in ambient ozone pollution attributable to climate change. The future ozone-related mortality burden in 104 Chinese cities is substantially reduced under RCP4.5, which includes effective emission mitigation strategies [
There are several limitations in our study. First, only 1 global chemistry–climate model was applied to project future ozone concentrations. The choice of climate and air quality models has been found to dominate the uncertainty (approximately 48% to 97%) in projecting ozone-related health impacts [
Overall, we have shown that the future mortality burden for the Chinese population of short-term exposure to ambient ozone pollution will increase under plausible scenarios of climate and emission change and population aging. Population aging contributes substantially to the future ozone-related acute excess mortality. People with cardiovascular disease may require more care in a changing climate. Cold season ozone may play a more important role than currently recognized in future changes of ozone-related mortality in China. The differences in the direction and magnitude of future changes in ozone-related mortality attributable to climate and emission change under RCP4.5 and RCP8.5 suggest that climate change mitigation actions, such as preventing atmospheric methane from doubling, are needed to prevent a rising health burden from exposure to ambient ozone pollution in China.
The authors are solely responsible for the content of this paper and do not represent the official view of the EPA.
A total of 778 national ambient ozone monitoring sites were involved in this study. Note that many sites overlap due to their proximity.
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Changes of annual anthropogenic emission density used in the GFDL-CM3 model in China (18°~48° N, 100°~128° E) over 2000–2050 under RCP4.5 and RCP8.5 for (A) nitrogen oxides (NOx) (Tg N/year), (B) nonmethane volatile organic compounds (VOCs) (Tg C/year), and (C) carbon monoxide (Tg CO/year).
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(A) Difference between mean ozone changes in 104 Chinese cities at coarse and fine resolution (0.25 × 0.25°). The horizontal line within each box represents the median concentration among 104 cities, the lower and upper boundaries of the box indicate the 25th and 75th percentiles, and the ends of the whisker lines indicate the maximum and minimum concentrations within 1.5 times the interquartile range from the upper and lower box boundaries. (B) Future changes (%) in ozone-related mortality by cause of death (cardiovascular, respiratory, and other causes of non-accidental deaths) based on coarse resolution ozone projections. RCP4.5 and RCP8.5 represent moderate and high global warming and emission scenarios, respectively. (C) Percent difference between cause-specific ozone-related acute excess mortality at coarse and fine resolutions. (D) Spatial distribution of percent difference between cause-specific ozone-related acute excess mortality at coarse and fine resolutions in 104 Chinese cities in 2053–2055 relative to 2013–2015 under RCP8.5. (E) Same as (D) but under RCP4.5.
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concentration–response function
empirical confidence interval
Geophysical Fluid Dynamics Laboratory chemistry–climate model CM3
maximum daily 8-hour average
nitrogen oxide(s)
parts per billion
probability interval
Representative Concentration Pathway
shared socioeconomic pathway