Environment International 75 (2015) 21–32

Contents lists available at ScienceDirect

Environment International journal homepage: www.elsevier.com/locate/envint

Review

Projecting future air pollution-related mortality under a changing climate: progress, uncertainties and research needs Lina Madaniyazi a, Yuming Guo b, Weiwei Yu b, Shilu Tong c,⁎ a b c

School of Public Health and Social Work, Queensland University of Technology, 10 Kelvin Grove, QLD 4059, Australia School of Population Health, University of Queensland, Herston QLD 4006, Australia School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia

a r t i c l e

i n f o

Article history: Received 17 April 2014 Received in revised form 23 October 2014 Accepted 24 October 2014 Available online xxxx Keywords: Projection Air pollutants Mortality Climate change

a b s t r a c t Background: Climate change may affect mortality associated with air pollutants, especially for fine particulate matter (PM2.5) and ozone (O3). Projection studies of such kind involve complicated modelling approaches with uncertainties. Objectives: We conducted a systematic review of researches and methods for projecting future PM2.5-/O3-related mortality to identify the uncertainties and optimal approaches for handling uncertainty. Methods: A literature search was conducted in October 2013, using the electronic databases: PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search was limited to peer-reviewed journal articles published in English from January 1980 to September 2013. Discussion: Fifteen studies fulfilled the inclusion criteria. Most studies reported that an increase of climate change-induced PM2.5 and O3 may result in an increase in mortality. However, little research has been conducted in developing countries with high emissions and dense populations. Additionally, health effects induced by PM2.5 may dominate compared to those caused by O3, but projection studies of PM2.5-related mortality are fewer than those of O3-related mortality. There is a considerable variation in approaches of scenario-based projection researches, which makes it difficult to compare results. Multiple scenarios, models and downscaling methods have been used to reduce uncertainties. However, few studies have discussed what the main source of uncertainties is and which uncertainty could be most effectively reduced. Conclusions: Projecting air pollution-related mortality requires a systematic consideration of assumptions and uncertainties, which will significantly aid policymakers in efforts to manage potential impacts of PM2.5 and O3 on mortality in the context of climate change. © 2014 Published by Elsevier Ltd.

Contents 1. 2. 3.

4.

Introduction . . . . . . . . . . . . . . Search strategy and selection criteria . . . Results . . . . . . . . . . . . . . . . 3.1. Main findings . . . . . . . . . . 3.1.1. Global projections . . . . 3.1.2. North America projections 3.1.3. Europe projections . . . . 3.1.4. East Asia projection . . . 3.2. Uncertainties . . . . . . . . . . 3.2.1. Scenarios . . . . . . . . 3.2.2. Models . . . . . . . . . 3.2.3. Health impact projection . Discussion . . . . . . . . . . . . . . . 4.1. Main findings . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

⁎ Corresponding author. E-mail addresses: [email protected] (L. Madaniyazi), [email protected] (Y. Guo), [email protected] (W. Yu), [email protected] (S. Tong).

http://dx.doi.org/10.1016/j.envint.2014.10.018 0160-4120/© 2014 Published by Elsevier Ltd.

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

22 22 22 22 22 23 23 23 23 23 26 26 27 27

22

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

4.2.

Uncertainties. . . . . . . . . . . 4.2.1. Scenarios . . . . . . . . 4.2.2. Models . . . . . . . . . 4.2.3. Health impact assessment . 4.2.4. Key uncertainties. . . . . 5. Conclusions. . . . . . . . . . . . . . . Acknowledgement . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

27 27 29 29 29 30 30 30

1. Introduction

2. Search strategy and selection criteria

Exposure to air pollutants has been linked to adverse health effects on both respiratory and cardiovascular systems, for example, increased airway reactivity, lung inflammation and increased respiratory symptoms (Bernard et al., 2001; Wong et al., 2006). Numerous studies indicated that exposure to air pollutants was related to increased risks of mortality and morbidity (Bell et al., 2005; Gryparis et al., 2004; Ito et al., 2005; Jerrett et al., 2009; Levy et al., 2005; Samoli et al., 2013; Shang et al., 2013; Zeger et al., 2008). All these findings indicated that air pollution remains a substantial public health problem, particularly in mega cities (Guo et al., 2013). Climatic conditions affect concentrations of air pollutants by modifying their emissions, aerosol photochemistry, transport and removal (Jacob and Winner, 2009). For example, increased temperature can increase concentrations of O 3 by accelerating the rates of photochemical reaction and higher biogenic volatile organic compound (VOC) emissions (Penrod et al., 2014). Thus, given that climate may change in the future, there is a growing interest in studying how air pollutants respond to a changing climate. Recently, changes in concentrations of air pollutants driven by scenarios of future emissions and/or weather patterns have been projected worldwide, with most projections for North America and Europe (Anderson et al., 2001; Aw and Kleeman, 2003; Bell et al., 2007; Derwent et al., 2001; Hogrefe et al., 2004a,b; Johnson et al., 2001; Leung and Gustafson, 2005; Liao et al., 2006; Murazaki and Hess, 2006; Racherla and Adams, 2006; Steiner et al., 2006; Stevenson et al., 2000; West et al., 2007). Increased concentrations of air pollutants have been projected by most, with high variability across regions. The Intergovernmental Panel on Climate Change (IPCC) concluded that climate change would modify a variety of chemicals and processes that control air quality, and the net effects are likely to vary from one region to another (IPCC, 2007). Given that air pollution levels in the future are likely to increase (Fang et al., 2013; Selin et al., 2009; West et al., 2007), there is growing concern about its subsequent effects on public health. To date, there have been three reviews on the topic of climate change, air quality and health impacts (Ebi and McGregor, 2008; Kinney, 2008; Sujaritpong et al., 2014). Two of them were published in 2008 and only gave a broad overview of the outcomes of studies (Ebi and McGregor, 2008; Kinney, 2008). However, with the development of climate-air quality projection model systems, more studies have been conducted since then, and more issues in this area have been recognised and explored. Another recent review mainly focused on the methods that have been applied to quantify how future climate change will modify air pollution-related health effects (Sujaritpong et al., 2014), but the findings of those studies are not fully discussed and summarized. Thus, to fill these knowledge gaps and make recommendations, we review studies on the projection of mortality in association with PM 2.5 and O 3 , because these two air pollutants have been studied most. Additionally, we closely examined the uncertainties in projecting air pollution-related mortality.

A literature search was conducted using the electronic databases PubMed (National Library of Medicine 2010), Scopus and ScienceDirect (Elsevier 2010), ProQuest (2010), and Web of Science (Thomson Reuters 2010). The search was limited to journal articles published in English from January 1980 through August 2013. The key words used were PM2.5, O3, climate change, mortality, death, and projection. References and citations of the articles identified were manually inspected to ensure that all relevant articles were included. Three inclusion criteria were used to select articles. First, articles had to include at least one projection of future air pollution-related mortality; studies of the climate change impact solely on future infectious diseases or air pollution were excluded. Second, in order to obtain authoritative information, this review included only peer-reviewed journal articles; books, reports, and conference abstracts were excluded. Third, we included only quantitative, empirical studies; reviews and qualitative studies were excluded.

3. Results As summarized in Table 1, we found 15 studies that compared air pollution levels and related mortality, and also simulated future mortality projections.

3.1. Main findings Table 1 shows the key findings of the reviewed studies in detail. Among the fifteen studies, three had a global focus, and eight focused on North America, three on Europe, and one on East Asia. Four of the fifteen studies focused on both PM2.5 and O3-related mortality; nine on O3-related mortality; one on PM2.5-related mortality; one on multi-pollutant (including CO, O3, NO2, SO2 and Suspended particles (SP))-related mortality. Among those studies on the projection of O3-related mortality, five of them applied a threshold level for O3 to project related mortality; seven of them only projected O3-related mortality during summer. Increased air pollution-related mortality has been projected by most of these studies; and changes in mortality induced by PM2.5 were larger than those caused by O3. However, the results spanned a wide range. The scenarios and models varied from one study to another.

3.1.1. Global projections Three global projections reported increasing air pollution-related mortality in the future. Among them, Fang et al. (2013) focused on both PM2.5 and O3, while the other two (Selin et al., 2009; West et al., 2007) focused on O3 only. Although different scenarios of climate change, emission changes and models were applied, the largest increase in air pollution-related mortality has been projected in developing areas, such as Asia and central Africa. In addition, air pollution-related mortality has been projected to increase also in some developed areas, such as the eastern United States (U.S.) and parts of Europe.

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

3.1.2. North America projections Eight projection studies were conducted in North America, including seven in the U.S. and one in Canada. Among those studies in the U.S., three projections were conducted across the U.S. (Post et al., 2012; Tagaris et al., 2009, 2010), while other four were focused on the southeast U.S. (Bell et al., 2007; Chang et al., 2010), two counties in Washington State (Jackson et al., 2010) and New York (Knowlton et al., 2004), respectively. Only Tagaris et al. (2009, 2010) projected PM2.5-related mortality in North America. Generally, the net change in pollution-related mortality has been projected to increase nationwide, whereas the results varied spatially throughout regions. Tagaris et al. (2009, 2010) projected increased PM2.5-related mortality in the north-eastern U.S., increased O3-related mortality in south U.S. but slightly increased or even decreased O3related mortality in the northern U.S. Post et al. (2012) applied seven modelling systems, where a climate change model is linked to an air quality model, five population projections, and multiple concentration–response functions to project O3-related mortality. They found that O3-related mortality would increase in the whole nation, the northeast and southeast U.S. for most of the modelling systems, while O3related mortality would less increase or even decrease in the western U.S. under most of the modelling systems. Instead of focusing on the whole U.S., Chang et al. (2010) and Bell et al. (2007) have studied and projected increased O3-related mortality in 18 urban communities in the southeast U.S. and 50 cities in the east U.S., respectively. Although different scenarios and models were applied in these studies, in general, air pollution-related mortality in the east U.S. is more likely to increase under a changing climate. In addition, by comparing the differences in O3-related mortality impacts between urban and suburban counties within the larger New York metropolitan region, Knowlton et al. (2004) discovered that counties with the highest percent increases in projected O3 mortality spread beyond the urban core into less densely populated suburban counties. In four Canadian cities, Cheng et al. (2008b) applied a synoptic weather-typing approach to project impacts of future extreme temperatures and air pollution on mortality in two time periods (2049–2059 and 2070–2089). Five pollutants, including O3, CO, NO2, SO2, and suspended particles (SP), related mortality was combined and projected to increase about 20–30% by the 2050s and 30–45% by the 2080s, which would be driven largely by increases in the effect of O3 (Cheng et al., 2008b). However, detailed estimation of different pollutant-related mortality has not been provided. 3.1.3. Europe projections There are three projection studies in Europe. Only Tainio et al. (2013) focused on PM2.5-related mortality, while other two concentrated on O3-related mortality. Orru et al. compared O3-related mortality of current period (1990–2009), near future period (2021–2050), and further future (2041–2060) with baseline (1961–1990) in Europe (Orru et al., 2013). The results varied throughout countries. The other two studies focused on PM2.5-related mortality in Poland and O3-related mortality in the UK, respectively. Increased O3-related mortality was projected in Poland by Orru et al. (2013), while decreased PM2.5 levels and related mortality have been projected in Poland by Tainio et al. (2013). Doherty et al. (2009) have described a projection to quantify the burden of heat and O3 on mortality in the UK, both for the present-day and under future emission scenarios. However, results have not been presented by Doherty et al. (2009). 3.1.4. East Asia projection There is only one air pollution-related mortality projection in Asia. Nawahda et al. (2012) projected the air pollution-related premature mortality under a changing climate, and found that for future scenarios of the year 2020, including policy succeed case (PSC), reference (REF), and policy failed case (PFC), the estimated annual air pollution-related premature mortality rates are 451,000, 649,000, and 1,035,000

23

respectively; and the effect of PM2.5 on human health was greater than the effect of O3 for the age group of 30 years and above. 3.2. Uncertainties Projecting air pollution-related mortality requires the following steps (Sujaritpong et al., 2014): a) climate change projections: general circulation models (GCMs) will be used to project future climate conditions under greenhouse gas (GHG) emission scenarios; b) air pollution level projections: the output from climate projections will be coupled with air quality models (AQMs) to project future air pollution levels under air pollution emission scenarios; c) health-impact projections: the projected air pollution levels will be combined with concentration–response function (CRF), population, and mortality to project future air pollution-related mortality. Therefore, such a projection study is inherently complicated and involves numerous uncertainties which lie in each step. Uncertainties and relevant solutions adopted in each of the 15 studies were outlined in Fig. 1 and summarized in Table 2. 3.2.1. Scenarios Projecting future air pollutants under a changing climate requires assumptions for both GHG and air pollution emission scenarios. The uncertainties from scenarios have been recognised by all studies but only been considered by some of them. The only way adopted by these studies to quantify this uncertainty is using multiple scenarios to cover a wide range of the possible future. However, most studies included in this review only considered one GHG scenario and assumed static future pollution emission levels. For GHG emissions, the IPCC has defined a set of 40 SRES scenarios that covered a wide range of the main driving forces of future GHG emissions (IPCC, 2007). These scenarios are structured in four major families labelled A1, A2, B1 and B2 (Nakicenovic and Swart, 2000). In the existing studies, most applied scenarios were A1B and A2. To isolate the effect of climate change from the effect of future air pollution emission changes on air pollution levels, most studies assumed static anthropogenic emission levels in the future. For those projection studies (Cheng et al., 2008b; Doherty et al., 2009; Jackson et al., 2010; Nawahda et al., 2012; Selin et al., 2009; Tagaris et al., 2009) that considered the alternation of future air pollution emissions, the following approaches have been adopted to project future pollution emission changes: a) set up emission projections consistent with the storylines identified in the IPCC SRES scenarios; and b) projected under the scenarios considered the trend of policies, technologies, and other potential factors. Most applied scenarios in this approach were “Current Legislation” (CLE) scenario and “Maximum technically Feasible Reduction” (MFR) scenario developed by the Institute for Applied Systems Analysis (IIASA). In order to explore the impact of future air pollution emission changes on air pollution levels under a changing climate, three reviewed studies compared the results with and without considering the changes in future air pollution emissions (Cheng et al., 2008b; Knowlton et al., 2004; Selin et al., 2009). However, the results were inconsistent. Knowlton et al. (2004) found that estimates of total O3-related mortality in 31 counties in Connecticut, New York and New Jersey were slightly smaller under the impact of both climate change and anthropogenic increases than under climate change alone, especially in the most densely populated urban counties. We compared O3-related mortality with and without emission changes of O3 precursors for 31 counties reported by Knowlton et al. (2004), and the differences ranged from − 13.3% to 4.6%, with the most decreases in the most densely populated urban counties (e.g., New York); but results from compared T-analysis showed that the differences were statistically insignificant (P N 0.05). Selin et al. (2009) projected global O3-related mortality and discovered that in the developing regions, population-weighted concentrations of O3 and related mortality generally increased more due to the precursor emission changes than climate change, but increased slightly and even decreased

24

Reference

Setting

Study period

Mortality

Ozone exposure

Projection results

Global projections Fang et al. (2013) (1)

10 world regions

1981–2000, 2081–2100

The 6-month ozone season average of the 1-h daily maximum ozone concentration

In 21st century climate change increases global all-cause premature mortalities associated with PM2.5 by approximately 100,000 deaths and respiratory disease mortality associated with O3 by 6300 deaths annually.

Selin et al. (2009)

16 world regions

1999–2001, 2049–2051

PM2.5 and O3 related mortality and years of life lost (YLL) both with and without a low concentration threshold O3 related

The health costs due to global O3 pollution above pre-industrial levels by 2050 will be $580 billion and that mortalities from acute exposure will exceed 2 million.

West et al. (2007)

10 world regions, including Asia, Australia, and Middle East.

2000, 2030

O3 related

Population-weighted annual mean afternoon (1300–1700 h local time) O3, and populationweighted annual mean afternoon O3 above 10 ppb Population-weighted annual 8-h daily maximum O3 over 25 ppb(v)

North America projections Post et al. (2012) U.S.

2000, 2050

O3 related

Tagaris et al. (2010)

U.S.

2001,2050

O3 and PM2.5 related mortality due to precursor emissions under the influence of climate change

Chang et al. (2010)

19 urban communities in South-eastern United States Two areas in Washington State: King County, Spokane County

1987–2000, 2041–2050

O3 related

1997–2006, 2045–2054

Heat events and O3 related

Jackson et al. (2010)

Large increase in O3 was associated with approximately 500,000 additional deaths. Using a threshold of 25 ppbv, 191,000 deaths worldwide could be avoided using currently enacted legislation, and 458,000 deaths could be avoided using maximum feasible reduction technologies.

Maximum daily 8-h average O3 concentration from June to August

Different combinations of methodological choices produced a range of estimates of national O3-related mortality from roughly 600 deaths avoided as a result of climate change to 2500 deaths attributable to climate change (although the large majority produced increases in mortality). Results have been shown in the projection of Tagaris et al. (2009). Additionally, Daily maximum 8-h O3 levels the result here suggests that states with high emission rates and significant premature mortality increases induced by PM2.5 will substantially benefit in the future from SO2, anthropogenic NOx and NH3 emissions reductions while states with premature mortality increases induced by O3 will benefit mainly from anthropogenic NOx emissions reduction. Daily maximum 8-h O3 concentration in May to An increase of 0.43 ppb in average O3 concentration corresponds to a 0.01% September increase in mortality rate and 45.2 premature deaths.

Daily 8-h maximum O3 in May to September

By mid-century the non-traumatic mortality rate and cardiopulmonary death rate per 100,000 related to O3 were projected to increase.

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

Table 1 Characteristics of studies that projected PM2.5 and O3-related mortality under a changing climate.

Tagaris et al. (2009)

USA

2001, 2050

O3 and fine particulate matter related

Daily maximum 8-h O3 levels

Cheng et al. (2008b)

4 Canadian cities

1981–2000, 2040–2059, 2070–2080

Daily maximum 1-h O3 levels

Bell et al. (2007)

50 U.S. eastern cities 1993–1997, 2053–2057 31 counties in New 1990s and York city, USA 2050s

Air pollutants (including CO, O3, NO2, SO2 and Suspended particles (SP))-related non-traumatic mortality O3 related

Knowlton et al. (2004)

Poland

Orru et al. (2013)

29 countries in Europe

Doherty et al. (2009)

15 conurbations in England and Wales

East Asia projection Nawahda et al. (2012)

East Asian

1990s, 2040s, 2090s 1961–1990, 1990–2009, 2021–2050, 2040–2060 2003, 2005, 2006, 2030

2000, 2005, 2020

O3 related

Hourly ambient concentrations of ground-level O3 from June–August Daily 1-hr maximum O3 concentration from June–August; Sensitivity analysis about an O3 threshold value of 20 ppb.

Elevated O3 levels correspond to approximately a 0.11% to 0.27% increase in daily total mortality. Considering climate change alone, there was a median 4.5% increase in O3related acute mortality across the 31 counties. Incorporating O3 precursor emission increases along with climate change yielded similar results. When population growth was factored into the projections, absolute impacts increases substantially. Counties with the highest percent increases in projected O3 mortality spread beyond the urban core into less densely populated suburban counties.

PM2.5 related

N/A

O3 related

The sum of O3 daily 8-h max Means Over 35 ppb(v) (SOMO35) for summer and winter separately

PM2.5-related premature deaths would be 35,800 and 34,900 per year in 2050 and 2100, respectively. Increasing O3-related mortality was projected for most countries. Decreasing O3-related mortality was projected for a few countries.

Heat and O3 related

Two-day means of daily maximum 8-h O3 from Did not present. May–September

O3 and PM2.5 related

The sum of O3 daily maximum 8-h mean concentrations above 35 ppb

For future scenarios of the year 2020, the estimated annual premature mortality associated with both PM2.5 and O3 ranged from 451,000 to 1,035,000; the effect of PM2.5 is greater than the effect of O3 for the age group of 30 years and above.

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

Europe projections Tainio et al. (2013)

Nationally the analysis suggests approximately 4000 additional annual premature deaths due to climate change impacts on PM2.5 VS 300 due to climate change-induced O3 changes. However, the impacts vary spatially. Increased premature mortality due to elevated O3 concentrations will be offset by lower mortality from reduction in PM2.5 in 11 states. Air pollutant-related mortality could increase about 20–30% by the 2050s and 30–45% by the 2080s, which largely driven by increment in O3 effect.

25

26

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

Uncertainties

Scenarios

Models

Health

GHG Scenarios; Air Polluon Emission Scenarios

Soluons: Muliple Scenarios

CRF; Populaon; Mortallity

GCMs; AQMs

(Size and Demographic)

Soluons:

Soluons:

1) Mulple Models;

1) CRF, populaon and mortality obtained from the area of insterest;

2) Downscaling Methods

2) Projected populaon and mortality

Fig. 1. Uncertainties in the projections of air pollution-related mortality.

due to the effect of climate change alone. Thus, the impact of air pollution emission changes on O3-related mortality may have regional variation. Though Tagaris et al. (2010) estimated the relative contributions of PM2.5 precursor emissions to air pollution-related mortality modulated by climate change, they did not report separate results for PM2.5-related mortality due to climate change alone, which makes it impossible for us to compare effects of climate change and precursor emission's changes on PM2.5-related mortality.

3.2.2. Models The GCMs and AQMs could be used to simulate future climate and air pollution levels, respectively. There are different types of GCMs, depending on whether they are incorporated dynamics from the atmosphere, the ocean, or both. Likewise, there are various options for AQMs including numerical or empirical models. A number of numerical climate and air quality models with various levels of offline and online coupling between the chemistry and atmospheric dynamics have been developed to investigate the interactions between climate and air quality (Langner et al., 2012). Most studies we reviewed used numerical offline global or regional Chemical Transport Models (CTMs). All GCMs attempt to accurately simulate the process of climate change. This means different GCMs represent different cases of future climate change, and generate different projections of climate change, even though using the same hypothesis of future green gas emission (IPCC, 2007). Similar to GCMs, different AQMs could produce different air quality projections. Thus, models are another source of uncertainties. Among all the reviewed projection studies, two approaches have been used to quantify and control the uncertainties coming from models: multiple models and downscaling techniques. Using multiple GCMs and AQMs makes it possible to quantify the uncertainties caused by models. However, in our review, only Post et al. (2012) applied 7 different climate change-air quality modelling systems (shown in Table 2) to project future climate separately: By decomposing the total variability in estimated mortality into the variability due to the chosen GCMs– AQMs, population projection, CRF used and interaction between these choices, they found that GCMs–AQMs could be the source of the greatest uncertainty.

Increasingly, more studies focus on the future air pollution-related mortality on a local scale. However, spatial resolution of GCM results is too coarse to be applied directly in the assessment at a local scale. Thus, downscaling techniques have been used to control the uncertainties from the large-scale models. The downscaling methods that have been most widely used include dynamical modelling by nesting a regional climate model (RCM) within a GCM (Kim et al., 2000; Yarnal et al., 2000), statistical or empirical transfer functions that relate local climate to GCM output, using either analogue methods (circulation typing), regression analysis, or neural network methods (Cheng et al., 2008a). Most studies we reviewed used dynamic downscaling techniques. Only Cheng et al. (2008b) used the regression-based statistical downscaling approach: a number of regression models have been used to downscale daily GCM outputs to four station-scale hourly data; these models performed very well in deriving station-scale hourly/daily climate information (most model R2s N 0.95). 3.2.3. Health impact projection The basic health impact projection function is described as Mortality = Air Pollution Levels ∗ Mortality Effect Estimate ∗ Mortality Incidence ∗ Exposed Population, where Mortality Effect Estimates is an estimate of the percentage change in mortality due to one unit change in the concentration of an air pollutant, which could also be described as concentration–response function (CRF). The CRF, mortality incidence and exposed population may be different among areas and populations. They may also change over time with socioeconomic, demographic characteristics, behavioural and political changes. Thus these four factors will produce uncertainties. The most commonly adopted method to reduce these uncertainties was using regional data to obtain CRF, mortality incidence and exposed population. However, only four projection studies used CRF which was derived from the U.S. or EU to the area of interest (Fang et al., 2013; Nawahda et al., 2012; Selin et al., 2009; West et al., 2007). Since these four factors may change over time, the second way to reduce this uncertainty is to project CRF, mortality incidence and exposed population in the future. Among the projection studies we reviewed, mortality incidence and exposed population have been projected only in a few studies (Jackson et al., 2010; Knowlton et al., 2004; Nawahda et al., 2012; Post et al.,

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

2012), while changes of CRF in the future have not been considered in any of them. 4. Discussion There is growing interest in studying the potential health effects of air pollutant in association with future weather patterns (Patz et al., 2000). Projecting future air pollution-related mortality under climate change requires projections of future climate changes, future air pollution level changes, and health impact analysis. Several studies have been conducted to project future air pollution-related mortality. Although each of them has limitations, they provide an insight concerning projections of air pollution-related mortality under a changing climate. 4.1. Main findings According to those projections in the global scale, the largest increased air pollution-related mortality is more likely to happen in those areas with large precursor emissions and/or tropical and/or developing areas, such as the eastern U.S., central Africa, and Asia. These regions are usually highly populated and hence, increases in air pollutants will adversely impact on human health. Among these areas, the eastern U.S. has been studied most, due to the rich resources of the regional climate-air pollutant models and evidence for health impact analysis, for example, CRF. However, there is rare evidence in the developing regions with large emissions and dense population, for instance, Asia. Though increased air pollution-related mortality has been projected in most reviewed studies, the patterns of PM2.5 and O3 related mortality were different. The patterns could be affected by choices of scenarios and the use of GCM–AQMs. However, even projecting PM2.5 and O3 under the same scenario and GCM–AQM, the pattern of PM2.5-related mortality is still different from that of O3-related mortality. For example, Tagaris et al. (2009) projected increased PM2.5-related while decreased O3-related mortality in the northeast U.S., because PM2.5 levels were predicted to increase in the Great Lakes area, and overall across the north-eastern U.S., while annual O3 levels were predicted to slightly decrease across the northern section of the U.S., and increasing across the southern sections of the U.S. The sensitivity of PM2.5 and O3 to precursors may vary with regions: PM2.5 concentrations are more sensitive to NOx, SO2 and NH3 emission, while O3 concentrations are more sensitive to NOx emission but less sensitive to SO2 and NH3 emission (Tagaris et al., 2010). Another reason might be the different sensitivities of PM2.5 and O3 to meteorologic factors (Jacob and Winner, 2009): 1) temperature is the most important meteorological variable affecting O3 concentrations; and 2) due to the diversity of PM2.5 components, the response of PM2.5 to meteorologic variables is more complicated than that for O3. For example, increasing temperature could increase sulphate concentrations but decrease nitrate. However, for some already polluted regions with large emissions, for example, east and south China, both PM2.5 and O3 levels and related mortality are more likely to increase in the future (Fang et al., 2013; Nawahda et al., 2012). Compared with O3, there was less evidence exploring the potential impact on climate change-induced human health effects caused by changes in PM2.5 concentration. This might be related to the limited studies currently addressing the potential impact of climate change on PM2.5 levels (Tagaris et al., 2009). Jacob and Winner (2009) summarized in their review that there were large uncertainties regarding model projections of future PM2.5 levels induced by climate change, because precipitation frequency and mixing depth are important driving factors but projections for these variables are often unreliable. They also concluded from different studies that annual mean PM2.5 concentration was projected to change by ± 0.1–1 μg/m3 in Europe and North America, with little consensus between studies (Jacob and Winner, 2009). However, Fang et al. (2013) projected substantially increased surface PM2.5 concentrations near source regions in 2081–2100 under

27

the A1B scenario (e.g., East Asia, eastern U.S., and northern India) which are highly populated. Thus, more attention needs to be paid to PM2.5 concentration changes in highly populated areas near emission sources. In our review, there are four projection studies that focused and compared PM2.5 and O3 related mortality on a global scale, and for the U.S. and East Asia, respectively; all of them reported greater changes in health effects induced by PM2.5 compared to those caused by O3, since the CRF of PM2.5-related mortality was larger than that of O3related mortality (Bell et al., 2004; Pope et al., 2002). Thus, in the future, PM2.5 related health outcomes under a changing climate need to be explored further. Most reviewed projections only considered O3-related mortality in summer and PM2.5-related mortality in the whole year. However, this might underestimate or overestimate the results for the following reasons: a) some areas have a longer O3 season, and it has been projected that in the future the differences of O3 levels between summer and other seasons may diminish under the impact of climate and emission changes (Tagaris et al., 2007); b) there is an increasing concern over exposures (humans and others) to O3 at lower levels (Tagaris et al., 2009). Some studies showed evidence that O3-mortality relationship holds at low concentrations, well below current national standards, and questioned whether a low-concentration threshold exists (So and Wang, 2003); c) PM2.5 concentrations generally are higher during winter and autumn and lower during spring and summer, which has also been projected for the future (Tagaris et al., 2007); and d) seasonal variation of effects of air pollutants on human health has been reported in several studies (Chen et al., 2013; Kan et al., 2008; Peng et al., 2005), which also varies among regions. For example, many studies reported health risks of exposure to O3 in summer and/or warm seasons (Guo et al., 2014), while several studies in China showed higher risks in the cool season than in the warm season (Kan et al., 2008; Wong et al., 2001). Thus, to better project PM2.5 and O3 in the future under a changing climate, both seasonal and annual analyses are important. There has always been a concern that the health risks of air pollution levels may be influenced by meteorological conditions, especially temperature (Li et al., 2011; Ren and Tong, 2006; Ren et al., 2008; Vanos et al., 2013, 2014a,b; Vanos and Cakmak, 2014). People may be exposed to extreme heat events and increased concentrations of air pollutants at the same time, thus, it is essential to understand and estimate the combined effects of heat and air pollutants (WHO RegionaI Office for Europe, 2009; Huang et al., 2011; Vanos and Cakmak, 2014). Furthermore, simultaneous exposure to high temperature and air pollution has been projected to be more frequent in the future under a changing climate (Clark et al., 2006). However, no projection study has considered the interaction effect of air pollution and temperature on mortality, which might add uncertainties to the results. 4.2. Uncertainties Most of the existing studies projected increased air pollution-related mortality in the future. However, the projection outcomes were various (Post et al., 2012; Tagaris et al., 2009). This may have resulted from the different climate change-air quality modelling systems, scenarios, and health-impact analysis, which are also the main sources of the uncertainties. 4.2.1. Scenarios The scenarios provide a possible future of GHG and air pollution emissions. The uncertainty associated with future emissions has been recognised in the U.K. climate projections 2009 by giving probabilistic projections that correspond to each of the three different emission scenarios: high, medium, and low (Murphy et al., 2009). Though uncertainties from scenarios have been recognised by all studies, the only way that has been adopted to reduce the uncertainties is to use multiple scenarios.

28

Table 2 Uncertainties of studies that projected air pollutant-related mortality under a changing climate. Reference

Scenarios

Models

Health

Downscaling

The Space- time air pollutants Prediction Model

The Concentrationresponse function (Relative Risk)

Taken from The growth in population and its spatial distribution is epidemiological modelled in four world regions, according to SRES A2 studies. scenario. The CRF and mortality rate were assumed constant to 2030. time series Did not consider regressions

West et Three scenarios: al. (2007) SRES A2, CLE scenario, MFR

GCM

Did not consider

the LMDz-INCA chemistry-climate model

Doherty et al. (2009)

Weather forecasting models (WRF)

Dynamic Downscaling: Simulations were first performed at a 50 km by 50 km resolution for the larger European domain and these were used as boundary and initial conditions for further WRF simulations over the smaller UK domain at 5 km by 5 km resolution.

coupled WRF- EMEP4UK model is used to simulate daily surface temperature and ozone concentrations

Regional Atmospheric Modelling System (RAMS) ver. 4.3 (for the year 2020) and ver. 4.4 (for the years 2000–2005).

Dynamic Downscaling: Regional Atmospheric Modelling System (RAMS)

the Models-3 Community Multiscale Air Quality Modelling System coupled with the Regional Emission Inventory in Asia (CMAQ/REAS) for the years 1980–2020

Nawahda et al. (2012)

Three scenarios: optimistic (maximum feasible reduction, MFR), pessimistic SRES A2), and current legislation (CLE) Three scenarios: reference (REF) scenario; policy succeed case (PSC) scenario; policy failed case (PFC) scenario

Taken from WHO

Demographic Change

The distributed population for 2020 is estimated based on the population projections for 2015 by the Gridded Population of the World (GPWv3) and the estimated growth rate of 0.42% in East Asia for the period from 2015 to 2020 by the United Nations Department of Economic and Social Affairs/Population Division. The mortality rate stayed constant.

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

Climate Air Climate Change Change Pollutant Scenario Emission Scenarios

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

Similarly, Sujaritpong et al. (2014) indicated that using multiple future GHG emission scenarios was the favoured approach to address GHG emission uncertainty. Based on two projection studies (Cheng et al., 2008b; Knowlton et al., 2008) in North America, Sujaritpong et al. (2014) pointed out that only a small discrepancy of the health impacts between high and low GHG emission scenarios can be expected if the time horizon for the projections is not beyond the 2050s. However, whether the health impacts between high and low GHG emission scenarios would be small or not if the projection time is before the 2050s needs to be explored further based on more evidence in the future, especially when projecting different air pollution-related mortalities, since reactions and sensitivities of PM2.5 and O3 levels are different. Air pollution emission scenarios may also cause an uncertainty in the assessment of air pollution-related mortality. It is still unclear how much will the changes of air pollution emissions in the future affect air pollution-related mortality under a changing climate? Limited studies have considered this issue, and the results were inconsistent. Future studies need to consider this problem. However, this issue has been discussed by some other studies which only projected the future air pollution levels rather than projecting the health impact. For example, Penrod et al. (2014) found that anthropogenic emissions had a dominant effect over climate change on PM2.5 levels, with an important role in future O3 levels during summer. Similar findings also have been reported by Lam et al. (2011). Thus, due to the potential important impact of air pollution emission on future air pollutants' levels, it is necessary to consider changes in air pollution emissions when projecting future air pollution levels, especially for future PM2.5 levels. Also, sensitivity analyses with and without allowing changes in air pollution emissions should be conducted at the same time. 4.2.2. Models The uncertainties in models may arise from model parameters, or from structural uncertainties as some processes in the climate and air quality system are not fully understood or are impossible to resolve because of computational constraints (IPCC, 2007). Additionally, when projecting on a regional scale, due to the model size and complexity, the GCM models must have inevitably omitted some factors that affect regional climate (e.g., effect of the Great Lakes on southern Ontario weather) (Cheng et al., 2008b). Thus, projection data with less uncertainty at a higher spatial resolution may be more valuable. An ensemble of models and the downscaling technique are the most widely used approaches to addressing the model uncertainty. However, due to the requirement of massive computational resources (Sujaritpong et al., 2014), the single GCM combined with a dynamic downscaling method was used by many studies. This might be due to the accessibility and capacity of a single GCM which could best reproduce the climatology in the area of interest (Sujaritpong et al., 2014). For instance, GCM developed by the US Goddard Institute for Space Studies (GISS) has been applied in the projections of the U.S. (Knowlton et al., 2004; Tagaris et al., 2009). The Hadley Centre Coupled Model, version 3 (HadCM3) developed by a UK institution was used to project in Europe (Orru et al., 2013). Likewise, because of the high computational demands, most studies used a single AQM to project future air pollution levels. However, due to the uncertainty associated with model diverse simulation processes and functions, projections based on a single model could produce bias. The downscaling method, including dynamic downscaling and statistical downscaling, is required to project air pollutant concentrations on a regional scale. Among all the studies we reviewed, dynamic downscaling has been used more. However, the authors of these studies did not justify their choice in these terms. The benefits, drawbacks and applications of these two categories have been summarized and listed by Patz et al. (2005): generally, dynamic downscaling could simulate the climate mechanism; however, it is computationally expensive and sensitive to uncertain parameterisations, which also could introduce uncertainty at the same time; in contrast, statistical downscaling is much

29

cheaper, but it requires for rich data and assumes constant relationships between local and large-scale climate, which could not capture climate mechanisms. Thus, in order to obtain projection data with less uncertainty at a higher spatial resolution, firstly, appropriate downscaling methods should be applied, according to research aims and the targeted area. For example, for those data-rich areas without enough computer resources and professional expertise, statistical downscaling is more preferred. In addition, if applicable, multiple downscaling methods (i.e., statistical–dynamic combined downscaling method (Wood et al., 2004)) are recommended, instead of solely relying on a single downscaling method. 4.2.3. Health impact assessment Though GCMs and chemistry-climate models may inherently have a lot of uncertainties, several factors, including CRF, baseline mortality rate and exposed population, need to be considered when assessing future air pollution-related mortality. All these factors may vary among different regions and populations. However, projections on a global scale (Fang et al., 2013; Selin et al., 2009; West et al., 2007) and in East Asia (Nawahda et al., 2012) applied CRFs derived from the U.S. and EU to other regions, which have multiple limitations and produce uncertainties, because of the differences in baseline disease patterns and age distribution, health systems, pollutant levels and composition, and exposure modifiers between different regions. In addition, Post et al. (2012) found out that, except for modelling systems, CRF was the source of the greatest uncertainty. However, the evidence on CRF in some regions, e.g., Asia, is still limited. Since it may cause a lot of bias and uncertainties to just simply apply CRF from different regions, there is an urgent need to study and summarize the health effect of air pollutants locally. In addition, just using a central value of CRF could also result in parametric uncertainty. Normally, a value of CRF is reported with a 95% confidence interval (CI) by epidemiological studies. Thus, air pollutionrelated mortality in the future should be projected and reported with a corresponding 95% CI. For example, Fang et al. (2013) projected increased annual respiratory mortality associated with chronic O3 exposure by 6300 deaths with a 95% CI ranging from 1600 to 10,400. An alternative approach is to adopt CRF from different sources (e.g., literature review and model simulations) to provide a possible range in which the value of CRF could be anywhere. The current CRF, mortality rate and exposed population are often adopted as the baseline which is inappropriate due to the demographic changes, socioeconomic development, and adaptation strategies (Ebi, 2008; Gosling et al., 2009). For instance, a recent study in three major cities in Australia reported that the effect of particulate matter on mortality in Sydney and Brisbane has declined from 1993 to 2000 (Roberts, 2013). Moreover, since the demographic distribution may change in the exposed population, the CRF for age group-specific mortality would be preferred. Furthermore, population growth has been indicated to have a substantial influence on the estimates of health impacts. By comparing O3-related mortality under different population assumptions, Post et al. (2012) indicated that the assumption that the population in 2005 will be exactly the same as it was in the year 2000 (i.e., by using Census 2000 population estimates) produces estimates that are consistently lower than those based on population projections. In addition to assuming that population would remain constant in the 2050s, Knowlton et al. (2004) considered the population growth in the sensitivity analyses, and observed that population growth has the largest effect on projections of changing summer O3-related mortality, greater than the isolated effect of climate change alone upon O3 concentrations and related mortality. 4.2.4. Key uncertainties Given the diversity of the uncertainties, there is a need to explore the following two issues: what is the main source of the uncertainties? Which uncertainty could be most easily and strategically reduced

30

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

through additional research? Among all the studies we reviewed, two studies explored the first issue (Knowlton et al., 2004; Post et al., 2012), but no study has considered the second issue. Followed by two mortality assessments with and without changes of air pollution emissions of O3 and PM2.5, Knowlton et al. (2004) reported that the effect of population growth on projections of changing summer O3-related mortality is greater than the isolated effect of climate change alone upon O3 concentration and related mortality. In addition, Post et al. (2012) considered a range of potential sources of uncertainty, except for changes of air pollution emissions of O3 and PM2.5, and developed seven model systems. They compared the O3-related mortality projected by these seven model systems by variance analysis and concluded that the choice of the climate change and the air quality model reflected the greatest source of uncertainty (the percent of total sum of squares (ss) from modelling system was 48%), followed by the choices of CRF (the percent of total ss from CRF was 18%), with the other modelling choices having lesser but still substantial effects (Post et al., 2012). Similarly, Chang et al. (2014) found that uncertainty in CRF and climate model variability was predominant uncertainty when projecting O3-related excesses in asthma emergency department visits. The direction of air pollution-related mortality was decided by the model system, including scenarios, so uncertainties in the air pollution-related mortality could be driven predominantly by uncertainty in model systems. Thus, choosing a model system is the key to reducing uncertainty in the air pollution-related health impact projections. Projecting air pollution-related mortality requires a complex modelling system, which is highly uncertain. Which uncertainties could be reduced effectively through future researches? As discussed before, Knowlton et al. (2004) and Post et al. (2012) used sensitivity analysis and variance analysis to identify the parameters which have the strongest impact on the final output, respectively. However, these methods could not be able to answer this question. Uncertainties from complicated modelling systems do not only exist in projection studies of air pollution-related mortality, but also in other research areas about climate change, especially climate change policies, for example health co-benefit and cost–benefit analysis of climate change dynamics (Newbold and Marten, 2014; Rabl and Zwaan, 2009; Remais et al., 2014). The experts in these areas have tried to address the issue by using Value of Information (VOI) analysis. For example, in order to reduce uncertainty when estimating the health co-benefits of GHG mitigation strategies, Remais et al. (2014) recommended a VOI analysis to determine which new data will most likely yield more precise estimates. Newbold and Marten (2014) estimated VOI for three key parameters of climate integrated assessment models (IAMs) for climate damages to examine which uncertain components of IAMs should be studied preferentially. Yokota and Thompson (2004) have reviewed all 16 VOI applications used in environmental health risk management (EHRM), and concluded that VOI application remains largely demonstrative in EHRM, which mainly results from the complexity of solving VOI problems with continuous probability distributions as inputs in models. However, even though VOI analysis has not been used in projections of air pollution-related mortality yet, and still needs to be improved, we suggest that VOI may be a possible way to study the same issue in uncertainties lying in projections of air -related mortality, even though it has not been used in this research area yet. 5. Conclusions Air pollutant levels and related mortality are likely to increase under the scenarios with rapid increases in emissions of air pollutants, and even under the scenarios considering recently enacted legislation to impact air quality; if the currently available emission control strategies or technologies could be aggressively employed globally, regardless of the economic cost, the death burden of air pollutants may reduce in the future under a changing climate. However, even under such a scenario, air

pollution-related mortality may still increase in those developing regions with dense populations. A few studies have projected air pollution-related mortality under different climate change scenarios. More projections need to be conducted in developing regions, such as Asia, which simultaneously requires the development of regional climate change-air quality models and health effect of air pollutant analysis. In addition, PM2.5-related mortality has been projected to be larger than O3-related mortality; however, less research has been undertaken to project PM2.5-related mortality. Thus, more work is needed on this topic. Also, air pollutionrelated mortality should be projected not only in any specific season, but also for the whole season. Otherwise, the results might be under or overestimated. Although the methods used for projections are still in their early stages and have limitations and multiple uncertainties, the need for evidence-based projections of future health impacts of air pollutants under a changing climate is urgent. The wide range of estimated air pollution-related mortality attributable to climate change resulting from different methodological choices highlights the need to consider an ensemble of estimates, rather than relying on any one modelling system or a set of assumptions (Post et al., 2012). In addition to using multiple projection models and scenarios, projections on a regional scale, for example, county and city, may help to reduce the uncertainties, given that climate change and emission changes vary by area (Christensen et al., 2007; Tagaris et al., 2007). At the same time, sensitivity analyses are required to examine different assumptions underlying the primary assessments. Such research will undoubtedly contribute to assessing and managing the potential impacts of climate change on air pollution-related mortality. Also, the estimates of future air pollution-related mortality contain uncertainties that need to be carefully interpreted for policy implications (Huang et al., 2011). Acknowledgement We thank Dr. Adrian G Barnett for his comments on this manuscript. The authors declare they have no actual or potential competing financial interests. References Anderson, H., Derwent, R., Stedman, J., 2001. Air pollution and climate change. In: McMichael, A.J., Kovats, R.S. (Eds.), Health Effects of Climate Change in the UK. UK Department of Health, London, pp. 193–217 (Available: http://www.dh.gov.uk/ assetRoot/04/06/89/15/04068915.pdf [accessed 28 Feburary 2013]). Aw, J., Kleeman, M.J., 2003. Evaluating the first-order effect of intraannual temperature variability on urban air pollution. J. Geophys. Res. 108, 4365. Bell, M.L., McDermott, A., Zeger, S.L., Samet, J.M., Dominici, F., 2004. Ozone and short-term mortality in 95 U.S. urban communities, 1987–2000. JAMA 292, 2372–2378. Bell, M.L., Dominici, F., Samet, J.M., 2005. A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology 16, 436–445. Bell, M.L., Goldberg, R., Hogrefe, C., Kinney, P.L., Knowlton, K., Lynn, B., Rosenthal, J., Rosenzweig, C., Patz, J.A., 2007. Climate change, ambient ozone, and health in 50 U.S. cities. Clim. Chang. 82, 61–76. Bernard, S.M., Samet, J.M., Grambsch, A., Ebi, K.L., Romieu, I., 2001. The potential impacts of climate variability and change on air pollution-related health effects in the United States. Environ. Health Perspect. 109 (Suppl. 2), 199–209. Chang, H.H., Zhou, J., Fuentes, M., 2010. Impact of climate change on ambient ozone level and mortality in southeastern United States. Int. J. Environ. Res. Public Health 7, 2866–2880. Chang, H.H., Hao, H., Sarnat, S.E., 2014. A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change. Atmos. Environ. 89, 290–297. Chen, R., Peng, R.D., Meng, X., Zhou, Z., Chen, B., Kan, H., 2013. Seasonal variation in the acute effect of particulate air pollution on mortality in the China Air Pollution and Health Effects Study (CAPES). Sci. Total Environ. 450–451, 259–265. Cheng, C., Campbell, M., Li, Q., Li, G., Auld, H., Day, N., Pengelly, D., Gingrich, S., Klaassen, J., MacIver, D., Comer, N., Mao, Y., Thompson, W., Lin, H., 2008a. Differential and combined impacts of extreme temperatures and air pollution on human mortality in south–central Canada. Part I: historical analysis. Air Qual. Atmos. Health 1, 209–222. Cheng, C., Campbell, M., Li, Q., Li, G., Auld, H., Day, N., Pengelly, D., Gingrich, S., Klaassen, J., MacIver, D., Comer, N., Mao, Y., Thompson, W., Lin, H., 2008b. Differential and combined impacts of extreme temperatures and air pollution on human mortality in south–central Canada. Part II: future estimates. Air Qual. Atmos. Health 1, 223–235.

L. Madaniyazi et al. / Environment International 75 (2015) 21–32 Christensen, J.H., Hewitson, B., Busuioc, A., Chen, A., Guo, X., Held, I., et al., 2007. Regional climate projections. Climate change 2007: the physical science basis. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., et al. (Eds.), Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. Clark, R.T., Brown, S.J., Murphy, J.M., 2006. Modeling northern hemisphere summer heat extreme changes and their uncertainties using a physics ensemble of climate sensitivity experiments. J. Clim. 19, 4418–4435. Derwent, R.G., Collins, W.J., Johnson, C.E., Stevenson, D.S., 2001. Transient behaviour of tropospheric ozone precursors in a Global 3-D CTM and their indirect greenhouse effects. Clim. Chang. 49, 463–487. Doherty, R.M., Heal, M.R., Wilkinson, P., Pattenden, S., Vieno, M., Armstrong, B., Atkinson, R., Chalabi, Z., Kovats, S., Milojevic, A., Stevenson, D.S., 2009. Current and future climate- and air pollution-mediated impacts on human health. Environ. Health 8 (Suppl. 1), S8. Ebi, K., 2008. Healthy people 2100: modeling population health impacts of climate change. Clim. Chang. 88, 5–19. Ebi, K., McGregor, G., 2008. Climate change, tropospheric ozone and particulate matter, and health impacts. Environ. Health Perspect. 116, 1449–1455. Fang, Y., Mauzerall, D., Liu, J., Fiore, A., Horowitz, L., 2013. Impacts of 21st century climate change on global air pollution-related premature mortality. Clim. Chang. 1–15. Gosling, S.N., McGregor, G.R., Lowe, J.A., 2009. Climate change and heat-related mortality in six cities part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change. Int. J. Biometeorol. 53, 31–51. Gryparis, A., Forsberg, B., Katsouyanni, K., Analitis, A., Touloumi, G., Schwartz, J., Samoli, E., Medina, S., Anderson, H.R., Niciu, E.M., Wichmann, H.E., Kriz, B., Kosnik, M., Skorkovsky, J., Vonk, J.M., Dortbudak, Z., 2004. Acute effects of ozone on mortality from the “air pollution and health: a European approach” project. Am. J. Respir. Crit. Care Med. 170, 1080–1087. Guo, Y., Li, S., Tian, Z., Pan, X., Zhang, J., Williams, G., 2013. The burden of air pollution on years of life lost in Beijing, China, 2004–08: retrospective regression analysis of daily deaths. BMJ 347. Guo, Y., Li, S., Tawatsupa, B., Punnasiri, K., Jaakkola, J.J.K., Williams, G., 2014. The association between air pollution and mortality in Thailand. Sci. Rep. 4, 5509. Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., Rosenzweig, C., Goldberg, R., Kinney, P.L., 2004a. Simulating regional-scale ozone climatology over the eastern United States: model evaluation results. Atmos. Environ. 38, 2627–2638. Hogrefe, C., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., Rosenzweig, C., Goldberg, R., Gaffin, S., Knowlton, K., Kinney, P.L., 2004b. Simulating changes in regional air pollution over the eastern United States due to changes in global and regional climate and emissions. J. Geophys. Res. 109, D22301. Huang, C., Barnett, A.G., Wang, X., Vaneckova, P., FitzGerald, G., Tong, S., 2011. Projecting future heat-related mortality under climate change scenarios: a systematic review. Environ. Health Perspect. 119, 1681–1690. IPCC, 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK. Ito, K., Leon, S.F.D., Lippmann, M., 2005. Associations between ozone and daily mortality: analysis and meta-analysis. Epidemiology 16, 446–457. Jackson, J.E., Yost, M.G., Karr, C., Fitzpatrick, C., Lamb, B.K., Chung, S.H., Chen, J., Avise, J., Rosenblatt, R.A., Fenske, R.A., 2010. Public health impacts of climate change in Washington State: projected mortality risks due to heat events and air pollution. Clim. Chang. 102, 159–186. Jacob, D.J., Winner, D.A., 2009. Effect of climate change on air quality. Atmos. Environ. 43, 51–63. Jerrett, M., Burnett, R.T., Pope III, C.A., Ito, K., Thurston, G., Krewski, D., Shi, Y., Calle, E., Thun, M., 2009. Long-term ozone exposure and mortality. NEJM 360, 1085–1095. Johnson, C.E., Stevenson, D.S., Collins, W.J., Derwent, R.G., 2001. Role of climate feedback on methane and ozone studied with a Coupled Ocean–Atmosphere-Chemistry Model. Geophys. Res. Lett. 28, 1723–1726. Kan, H., London, S.J., Chen, G., Zhang, Y., Song, G., Zhao, N., Jiang, L., Chen, B., 2008. Season, sex, age, and education as modifiers of the effects of outdoor air pollution on daily mortality in Shanghai, China: the Public Health and Air Pollution in Asia (PAPA) Study. Environ. Health Perspect. 116, 1183–1188. Kim, J., Miller, N.L., Farrara, J.D., Hong, S.-Y., 2000. A seasonal precipitation and stream flow hindcast and prediction study in the Western United States during the 1997/ 98 winter season using a dynamic downscaling system. J. Hydrometeorol. 1, 311–329. Kinney, P.L., 2008. Climate change, air quality, and human health. Am. J. Prev. Med. 35, 459–467. Knowlton, K., Rosenthal, J.E., Hogrefe, C., Lynn, B., Gaffin, S., Goldberg, R., Rosenzweig, C., Civerolo, K., Ku, J.Y., Kinney, P.L., 2004. Assessing ozone-related health impacts under a changing climate. Environ. Health Perspect. 112, 1557–1563. Knowlton, K., Hogrefe, C., Lynn, B., Rosenzweig, C., Rosenthal, J., Kinney, P.L., 2008. Impacts of heat and ozone on mortality risk in the New York city metropolitan region under a changing climate. In: Thomson, M., Garcia-Herrera, R., Beniston, M. (Eds.), Seasonal Forecasts, Climatic Change and Human Health. Springer, Netherlands. Lam, Y.F., Fu, J.S., Wu, S., Mickley, L.J., 2011. Impacts of future climate change and effects of biogenic emissions on surface ozone and particulate matter concentrations in the United States. Atmos. Chem. Phys. 11, 4789–4806. Langner, J., Engardt, M., Baklanov, A., Christensen, J.H., Gauss, M., Geels, C., Hedegaard, G.B., Nuterman, R., Simpson, D., Soares, J., Sofiev, M., Wind, P., Zakey, A., 2012. A multi-model study of impacts of climate change on surface ozone in Europe. Atmos. Chem. Phys. 12, 10423–10440.

31

Leung, L.R., Gustafson, W.I., 2005. Potential regional climate change and implications to U.S. air quality. Geophys. Res. Lett. 32, L16711. Levy, J.I., Chemerynski, S.M., Sarnat, J.A., 2005. Ozone exposure and mortality: an empiric Bayes metaregression analysis. Epidemiology 16, 458–468. Li, G., Zhou, M., Cai, Y., Zhang, Y., Pan, X., 2011. Does temperature enhance acute mortality effects of ambient particle pollution in Tianjin city, China. Sci. Total Environ. 409, 1811–1817. Liao, H., Chen, W.-T., Seinfeld, J.H., 2006. Role of climate change in global predictions of future tropospheric ozone and aerosols. J. Geophys. Res. 111, D12304. Murazaki, K., Hess, P., 2006. How does climate change contribute to surface ozone change over the United States? J. Geophys. Res. 111, D05301. Murphy, J.M., Sexton, D.M.H., Jenkins, G.J., Boorman, P.M., Booth, B.B.B., Brown, C.C., et al., 2009. UK Climate Projections Science Report: Climate Change Projections. Met Office, Hadley Center, Exeter. Special Report on Emissions Scenarios. Intergovernmental Panel on Climate Change (IPCC). In: Nakicenovic, N., Swart, R. (Eds.), Cambridge University Press, Cambridge, UK. Nawahda, A., Yamashita, K., Ohara, T., Kurokawa, J., Yamaji, K., 2012. Evaluation of premature mortality caused by exposure to PM2.5 and ozone in East Asia: 2000, 2005, 2020. Water Air Soil Pollut. 223, 3445–3459. Newbold, S.C., Marten, A.L., 2014. The value of information for integrated assessment models of climate change. J. Environ. Econ. Manag. 68, 111–123. Orru, H., Andersson, C., Ebi, K.L., Langner, J., Astrom, C., Forsberg, B., 2013. Impact of climate change on ozone related mortality and morbidity in Europe. Eur. Respir. J. 41 (2), 285–294. Patz, J.A., Engelberg, D., Last, J., 2000. The effects of changing weather on public health. Annu. Rev. Public Health 21, 271–307. Patz, J.A., Campbell-Lendrum, D., Holloway, T., Foley, Jonathan A., 2005. Impact of regional climate change on human health. Nature 438, 310–317. Peng, R.D., Dominici, F., Pastor-Barriuso, R., Zeger, S.L., Samet, J.M., 2005. Seasonal analyses of air pollution and mortality in 100 U.S. cities. Am. J. Epidemiol. 161, 585–594. Penrod, A., Zhang, Y., Wang, K., Wu, S.-Y., Leung, L.R., 2014. Impacts of future climate and emission changes on U.S. air quality. Atmos. Environ. 89, 533–547. Pope III, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewshi, D., Ito, K., Thurston, G.D., 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287, 1132–1141. Post, E.S., Grambsch, A., Weaver, C., Morefield, P., Huang, J., Leung, L.Y., Nolte, C.G., Adams, P., Liang, X.Z., Zhu, J.H., Mahoney, H., 2012. Variation in estimated ozone-related health impacts of climate change due to modeling choices and assumptions. Environ. Health Perspect. 120, 1559–1564. Rabl, A., Zwaan, B., 2009. Cost–benefit analysis of climate change dynamics: uncertainties and the value of information. Clim. Chang. 96, 313–333. Racherla, P.N., Adams, P.J., 2006. Sensitivity of global tropospheric ozone and fine particulate matter concentrations to climate change. J. Geophys. Res. 111, D24103. Remais, J.V., Hess, J.J., Ebi, K.L., Markandya, A., Balbus, J.M., Wilkinson, P., Haines, A., Chalabi, Z., 2014. Estimating the health effects of greenhouse gas mitigation strategies: addressing parametric, model, and valuation challenges. Environ. Health Perspect. 122, 447–455. Ren, C., Tong, S., 2006. Temperature modifies the health effects of particulate matter in Brisbane, Australia. Int. J. Biometeorol. 51, 87–96. Ren, C., Williams, G., Mengersen, K., Morawska, L., Tong, S., 2008. Does temperature modify short-term effects of ozone on total mortality in 60 large eastern US communities? — an assessment using the NMMAPS data. Environ. Int. 34, 451–458. Roberts, S., 2013. Have the short-term mortality effects of particulate matter air pollution changed in Australia over the period 1993–2007? Environ. Pollut. 182, 9–14. Samoli, E., Stafoggia, M., Rodopoulou, S., Ostro, B., Declercq, C., Alessandrini, E., Diaz, J., Karanasiou, A., Kelessis, A.G., Le Tertre, A., Pandolfi, P., Randi, G., Scarinzi, C., ZauliSajani, S., Katsouyanni, K., Forastiere, F., 2013. Associations between fine and coarse particles and mortality in Mediterranean cities: results from the MED-PARTICLES project. Environ. Health Perspect. 121, 932–938. Selin, N.E., Wu, S., Nam, K.M., Reilly, J.M., Paltsev, S., Prinn, R.G., Webster, M.D., 2009. Global health and economic impacts of future ozone pollution. Environ. Res. Lett. 4, 044014. Shang, Y., Sun, Z., Cao, J., Wang, X., Zhong, L., Bi, X., Li, H., Liu, W., Zhu, T., Huang, W., 2013. Systematic review of Chinese studies of short-term exposure to air pollution and daily mortality. Environ. Int. 54, 100–111. So, K.L., Wang, T., 2003. On the local and regional influence on ground-level ozone concentrations in Hong Kong. Environ. Pollut. 123, 307–317. Steiner, A.L., Tonse, S., Cohen, R.C., Goldstein, A.H., Harley, R.A., 2006. Influence of future climate and emissions on regional air quality in California. J. Geophys. Res. 111, D18303. Stevenson, D.S., Johnson, C.E., Collins, W.J., Derwent, R.G., Edwards, J.M., 2000. Future estimates of tropospheric ozone radiative forcing and methane turnover — the impact of climate change. Geophys. Res. Lett. 27, 2073–2076. Sujaritpong, S., Dear, K., Cope, M., Walsh, S., Kjellstrom, T., 2014. Quantifying the health impacts of air pollution under a changing climate—a review of approaches and methodology. Int. J. Biometeorol. 58 (2), 149–160. Tagaris, E., Manomaiphiboon, K., Liao, K.J., Leung, L.R., Woo, J.-H., He, S., Amar, P., Russell, A.G., 2007. Impacts of global climate change and emissions on regional ozone and fine particulate matter concentrations over the United States. J. Geophys. Res. 112, D14312. Tagaris, E., Liao, K.J., Delucia, A.J., Deck, L., Amar, P., Russell, A.G., 2009. Potential impact of climate change on air pollution-related human health effects. Environ. Sci. Technol. 43, 4979–4988.

32

L. Madaniyazi et al. / Environment International 75 (2015) 21–32

Tagaris, E., Liao, K.J., DeLucia, A.J., Deck, L., Amar, P., Russell, A.G., 2010. Sensitivity of air pollution-induced premature mortality to precursor emissions under the influence of climate change. Int. J. Environ. Res. Public Health 7, 2222–2237. Tainio, M., Juda-Rezler, K., Reizer, M., Warchałowski, A., Trapp, W., Skotak, K., 2013. Future climate and adverse health effects caused by fine particulate matter air pollution: case study for Poland. Reg. Environ. Chang. 13, 705–715. Vanos, J.K., Cakmak, S., 2014. Changing air mass frequencies in Canada: potential links and implications for human health. Int. J. Biometeorol. 58, 121–135. Vanos, J.K., Cakmak, S., Bristow, C., Brion, V., Tremblay, N., Martin, S.L., Sheridan, S.S., 2013. Synoptic weather typing applied to air pollution mortality among the elderly in 10 Canadian cities. Environ. Res. 126, 66–75. Vanos, J.K., Cakmak, S., Kalkstein, L.S., Yagouti, A., 2014a. Association of weather and air pollution interactions on daily mortality in 12 Canadian cities. Air Qual. Atmos. Health 1–14. Vanos, J.K., Hebbern, C., Cakmak, S., 2014b. Risk assessment for cardiovascular and respiratory mortality due to air pollution and synoptic meteorology in 10 Canadian cities. Environ. Pollut. 185, 322–332. West, J.J., Szopa, S., Hauglustaine, D.A., 2007. Human mortality effects of future concentrations of tropospheric ozone. C. R. Geosci. 339, 775–783.

WHO RegionaI Office for Europe, 2009. Improving Public Health Response to Extreme Weather Heat-waves. EuroHEAT, Copenhagen. Wong, C.M., Ma, S., Hedley, A.J., Lam, T.H., 2001. Effect of air pollution on daily mortality in Hong Kong. Environ. Health Perspect. 109, 335–340. Wong, T.W., Tam, W., Yu, I.T.S., Wun, Y.T., Wong, A.H.S., Wong, C.M., 2006. Association between air pollution and general practitioner visits for respiratory diseases in Hong Kong. Thorax 61, 585–591. Wood, A.W., Leung, L.R., Sridhar, V., Lettenmaier, D.P., 2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim. Chang. 62, 189–216. Yarnal, B., Lakhtakia, M.N., Yu, Z., White, R.A., Pollard, D., Miller, D.A., Lapenta, W.M., 2000. A linked meteorological and hydrological model system: the Susquehanna River Basin Experiment (SRBEX). Glob. Planet. Chang. 25, 149–161. Yokota, F., Thompson, K.M., 2004. Value of information analysis in environmental health risk management decisions: past, present, and future. Risk Anal. 24, 635–650. Zeger, S.L., Dominici, F., McDermott, A., Samet, J.M., 2008. Mortality in the Medicare population and chronic exposure to fine particulate air pollution in urban centers (2000– 2005). Environ. Health Perspect. 116, 1614–1619.

Projecting future air pollution-related mortality under a changing climate: progress, uncertainties and research needs.

Climate change may affect mortality associated with air pollutants, especially for fine particulate matter (PM2.5) and ozone (O3). Projection studies ...
425KB Sizes 0 Downloads 6 Views