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Climate change and air pollution are two important environmental issues in China. The study aimed to model different scenarios to assess the health impacts related to ambient particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) from climate change and air pollution emission control in China. A regional meteorology-climate model was used to simulate the ambient PM2.5 concentrations in China in the 2010s and the 2040s under different scenarios (climate change scenario, air pollution emission control scenario, and climate change and emission control scenario). Furthermore, changes in mortality and years of life lost (YLLs), an indicator which considers life expectancy at death, were adopted to estimate PM2.5-related health impacts. The concentrations of PM2.5 were estimated to slightly increase in climate change scenario but decrease in emission control scenario in 2040s. PM2.5-related health impacts would increase in climate change scenario in the 2040s, although emission control would outweigh the influence of climate change. The findings suggest that more targets actions should be taken to confront challenges of exacerbated PM2.5 pollutions and its health impacts attributable to climate change in the future.
As the biggest global health threat of the 21st century, tacking climate change could be the greatest global health opportunity (1-2). There are multiple linkages connecting climate change and air quality, and climate change is expected to degrade air quality (3). Considering PM2.5 is one of the leading contributors to global disease burden (4), it is of great importance to predict future ambient PM2.5 concentrations and its related health impacts by considering both the near-term changes in climate conditions and the changes in anthropogenic pollutant emissions in China on interdecadal timescales. Nevertheless, evidence investigating the health impacts attributable to ambient PM2.5 from both climate change and air pollution emission control under different scenarios in China is still lacking.
In order to assess the combined effects of interdecadal climate change and anthropogenic emission reductions on ambient PM2.5, the Flexible Global Ocean-Atmosphere-Land System Model, Grid-point Version 2 (FGOALS-g2) decadal climate prediction and a Multi-Resolution Emission Inventory for China (MEIC) were used to drive a Weather Research Forecast Model Coupled with Chemistry (WRF-Chem) model to simulate the ambient PM2.5 concentrations in China during the 2010s and the 2040s in the national level and different districts under the Representative Concentration Pathway 4.5 (RCP4.5) scenario. The WRF-Chem model is a flexible and efficient atmospheric simulation model, the chemical module of this model mainly includes the emission, transport, photolysis, gaseous chemical reaction, deposition, aerosol dynamics, and chemical processes of air pollutants. It was used to simulate PM2.5 concentrations in the 2010s, which were set as baseline; and PM2.5 concentrations with only climate change and emission control, respectively, and under both scenarios in the 2040s in this study (5).
The burden attributable to PM2.5 under the scenarios above for ischemic heart disease (IHD), stroke, chronic obstructive pulmonary disease (COPD), and lung cancer was estimated using the integrated exposure–response functions (IER) for each cause of death, which have been used in a Global Burden of Disease study (6) and are based on studies of ambient air pollution, household air pollution, and second-hand smoke exposure and active smoking (7). Data of the annual average population size were collected from the China Statistical Yearbook, and the city-level proportions of different age groups were obtained from the 2010 census. Yearly mortality data in the mainland of China were obtained from the national death surveillance data, which originated from China CDC. The age-specific and cause-specific mortality rates were estimated based on the death surveillance points, and the proportions of cause-specific mortality in different districts and age groups were collected from the China Death Surveillance Data set. The deaths and YLLs attributable to ambient PM2.5 were then calculated by applying the year-specific, location-specific, and age-specific population-attributable fractions to the number of deaths and YLLs (8). Monte Carlo simulations were used to calculate the 95% confidence interval of death burden of PM2.5. Because the data used in the study were collected without any individual identifiers, the study was exempted from the Institutional Review Board of Peking University Health Science Center in Beijing.
The national PM2.5 concentrations in the 2010s and the changes of its predicted concentrations under different scenarios in the 2040s in China are presented in Figure 1. Climate change scenarios increased the PM2.5 concentrations in most regions, while emission control scenarios would decrease the PM2.5 concentrations at the national level. With the impact of both climate change and emission control, the PM2.5 concentrations at the national level would decrease from 28.05 µg/m3 in the 2010s to 18.75 µg/m3 in the 2040s, with a reduction percentage of 33.16%.
Figure 1.Baseline PM2.5 concentration in the 2010s and the changes of its predicted concentrations under different scenarios in the 2040s in China. (A) Baseline PM2.5 concentrations in the 2010s; (B) Changes of PM2.5 concentrations under climate change scenario in the 2040s; (C) Changes of PM2.5 concentrations under emission control scenario in the 2040s; (D) Changes of PM2.5 concentrations under both climate change and emission control scenarios in the 2040s.
Climate change scenario under RCP4.5 would aggravate the health impacts of PM2.5 pollutions on death and YLLs, while the emission control scenario would alleviate the health influence of PM2.5 from the 2010s to the 2040s in the national level. The attributable number of deaths related to ambient PM2.5 pollutions was estimated to be 1,278,734 in the national level in the 2010s, which comprised of 371,939, 610,694, 177,455, and 118,646 cases from IHD, stroke, COPD, and lung cancer, respectively. In the 2040s, the estimate would increase by 0.96% under the climate change scenario, while it would decrease by 32.20% under the emission control scenario. Considering both the impact of both climate change and emission control, there were an estimated 385,004 fewer deaths, with a reduction percentage of 30.11%. The corresponding YLL would increase by 0.85% under the climate change scenario, while it would decrease by 31.06% under the emission control scenario. The attributable YLLs would decrease from 16,328,977 in the 2010s to 11,577,480 in the 2040s considering both scenarios. There would be 4,751,497 fewer YLL at the national level, with a reduction percentage of 29.10%. The largest reduction number is stroke among the four major diseases (Figure 2).
Figure 2.Deaths and years of life lost from main types of diseases associated with ambient PM2.5 pollution in the national level in the 2010s (baseline) and different scenarios in the 2040s. (A) Deaths associated with PM2.5 (in thousands). (B) Years of life lost associated with PM2.5 (in thousands).
Abbreviations: COPD=chronic obstructive pulmonary disease; IHD=ischemic heart disease.Table 1 showed the prediction of deaths and YLLs from main types of diseases associated with ambient PM2.5 pollutions in different districts of China in the 2040s. Generally, the attributable deaths and YLLs from major diseases would increase in most of the districts in the climate change scenario, with the largest increasing percentage in the east region. While considering both climate change and emission control scenarios, the attributable deaths and YLLs would decrease in the 2040s compared with the 2010s, with the largest percentage change in the Northeast.
Disease Region Baseline (2010s) Emission control (2040s) Climate change (2040s) Both scenarios (2040s) Deaths Lung cancer Central 38,961(37,072−40,985) 21,519(20,212−22,825) 39,130(37,309−40,992) 22,109(20,752−23,487) East 27,963(25,783−30,140) 16,408(14,688−18,013) 29,371(27,182−31,550) 17,854(16,009−19,647) North 26,498(24,619−28,218) 14,537(13,202−15,761) 27,741(25,959−29,476) 16,217(14,873−17,596) Northeast 7,446(6,727−8,254) 3,410(2,917−3,866) 7,570(6,724−8,372) 3,325(2,819−3,817) Northwest 5,211(4,757−5,652) 3,886(3,509−4,274) 5,283(4,863−5,744) 3,976(3,616−4,353) South 12,568(11,544−13,715) 7,352(6,629−8,055) 12,349(11,302−13,432) 7,017(6,294−7,750) Subtotal 118,646 (114,756−122,448) 67,112(64,362−69,925) 121,444 (117,581−125,190) 70,498 (67,662−73,192) COPD Central 64,204(61,198−67,190) 35,962(33,880−38,073) 63,973(60,818−67,072) 36,224(34,063−38,449) East 36,447(33,740−39,034) 21,779(19,727−23,965) 38,210(35,595−41,120) 23,597(21,496−25,654) North 31,488(29,716−33,292) 17,800(16,458−19,084) 32,920(31,243−34,905) 19,710(18,206−21,074) Northeast 8,258(7,454−9,026) 3,964(3,460−4,499) 8,393(7,586−9,228) 3,869(3,354−4,396) Northwest 11,841(10,900−12,783) 8,919(8,180−9,672) 12,004(11,063−12,987) 9,117(8,342−9,864) South 25,217(23,769−26,741) 14,720(13,658−15,827) 24,395(22,823−25,911) 13,894(12,733−14,934) Subtotal 177,455(172,588−182,322) 103,144(99,239−106,638) 179,895(175,040−185,082) 106,411(102,670−110,041) Stroke Central 211,289(207,855−214,510) 143,301(140,430−145,945) 211,024(207,658−214,034) 145,453(142,741−148,320) East 134,204(130,650−137,631) 94,621(91,183−97,837) 137,556(133,848−141,212) 101,686(98,280−105,428) North 114,137(111,590−116,745) 77,724(75,660−80,055) 116,413(113,937−118,848) 85,351(83,266−87,702) Northeast 40,158(38,860−41,346) 17,860(17,105−18,580) 40,436(39,159−41,638) 17,379(16,647−18,099) Northwest 33,097(32,288−33,905) 26,975(26,238−27,812) 33,454(32,670−34,341) 27,579(26,891−28,303) South 77,808(75,814−79,792) 46,743(45,324−48,253) 75,754(73,877−77,502) 44,132(42,768−45,573) Subtotal 610,694 (604,864−616,772) 407,224(401,877−412,712) 614,637(608,880−620,705) 421,580(416,769−426,887) IHD Central 122,087(120,667−123,605) 94,903(93,821−95,959) 122,239(120,806−123,808) 96,030(94,922−97,213) East 86,372(84,339−88,472) 68,678(67,149−70,229) 88,168(86,186−90,257) 71,379(69,693−73,016) North 73,129(71,754−74,462) 56,570(55,614−57,606) 74,447(73,095−75,727) 59,453(58,444−60,418) Northeast 28,545(27,984−29,102) 19,549(19,127−19,945) 28,684(28,096−29,275) 19,166(18,780−19,593) Northwest 15,877(15,599−16,138) 13,954(13,732−14,182) 16,029(15,778−16,306) 14,209(13,986−14,415) South 45,929(44,978−46,852) 35,896(35,282−36,514) 45,404(44,460−46,285) 35,005(34,323−35,639) Subtotal 371,939(368,700−375,355) 289,550(287,271−291,731) 374,971(371,754−378,145) 295,242(292,987−297,421) Years of life lost Lung cancer Central 618,254(586,213−649,797) 341,506(319,484−362,722) 621,022(591,164−650,132) 351,005(330,626−373,382) East 418,525(382,735−449,965) 245,493(220,072−271,775) 439,634(403,395−473,071) 267,137(241,539−292,445) North 420,444(392,967−449,296) 231,199(213,427−249,338) 440,286(411,122−469,785) 257,814(238,033−278,906) Northeast 122,896(109,279−136,079) 55,989(48,557−63,878) 124,838(112,378−138,037) 54,451(46,232−62,246) Northwest 92,129(83,979−100,523) 68,858(62,224−75,353) 93,412(85,682−102,071) 70,491(64,039−76,854) South 195,933(180,046−211,995) 113,982(101,729−125,447) 192,213(176,544−209,435) 108,683(97,777−121,219) Subtotal 1,868,180(1,812,549−1,923,629) 1,057,027(1,016,384−1,099,968) 1,911,405(1,853,155−1,968,896) 1,109,581(1,066,029−1,153,705) COPD Central 533,842(508,080−559,411) 298,877(280,884−317,152) 531,693(506,880−555,737) 300,812(283,007−319,205) East 264,192(245,779−282,745) 157,595(143,103−171,801) 276,908(258,560−294,864) 170,783(156,431−185,607) North 249,773(235,613−264,032) 142,174(132,390−151,925) 261,227(247,682−275,951) 157,236(146,015−168,021) Northeast 68,880(62,685−75,530) 32,767(28,474−36,638) 69,885(63,360−76,504) 31,828(27,951−35,856) Northwest 115,897(10,6659−125,847) 87,489(80,382−94,952) 117,461(109,153−126,100) 89,434(82,145−96,694) South 201,664(190,662−214,335) 116,153(107,836−124,542) 194,344(182,096−205,559) 109,339(101,195−117,871) Subtotal 1,434,247(1,396,645−1,473,934) 835,055 (807,109− 862,422) 1,451,518(1,415,206−1,490,994) 859,433 (831,918−888,195) Stroke Central 2,897,759(2,862,356−2,933,572) 1,989,408(1,957,755−2,023,614) 2,894,476(2,857,322−2,928,761) 2,019,695(1,985,656−2,052,569) East 1,635,016(1,604,390−1,666,452) 1,166,149(1,135,143−1,192,614) 1,674,562(1,642,650−1,705,592) 1,249,500(1,219,969−1,277,281) North 1,512,628(1,488,675−1,536,426) 1,044,102(1,022,670−1,066,083) 1,542,562(1,520,764−1,565,874) 1,141,362(1,118,516−1,164,129) Northeast 576,933(562,761−591,702) 264,880(255,562−273,217) 580,051(566,909−594,491) 256,258(247,343−264,957) Northwest 524,131(512,919−534,952) 430,440(419,469−440,682) 529,729(517,994−540,703) 440,257(430,930−450,217) South 1,024,580(1,006,117−1,041,922) 626,070(613,184−638,980) 995,327(977,612−1,013,550) 591,710(579,543−604,362) Subtotal 8,171,047(8,112,361−8,227,274) 5,521,049(5,469,542− 5,570,697) 8,216,707(8,153,930−8,275,064) 5,698,783(5,647,037− 5,751,956) IHD Central 1,615,329(1,601,388−1,629,674) 1,278,244(1,268,105−1,289,128) 1,616,886(1,602,627−1,630,338) 1,291,823(1,280,616−1,303,193) East 1,014,773(1,000,467−1,028,710) 820,795(810,026−831,461) 1,034,273(1,020,591−1,048,177) 850,757(839,236−862,970) North 951,341(940,914−961,546) 750,993(743,038−759,030) 967,379(955,759−978,445) 786,134(778,072−794,507) Northeast 402,678(397,267−408,094) 280,576(276,317−285,381) 404,150(398,385−409,863) 274,587(269,975−278,978) Northwest 264,156(260,580−267,665) 234,337(231,380−237,418) 266,697(263,514−269,950) 238,737(235,792−241,603) South 607,226(600,696−614,201) 479,783(474,860−485,079) 599,035(592,333−605,730) 467,646(462,523−472,811) Subtotal 4,855,503(4,830,782−4,879,767) 3,844,728(3,825,368−3,862,978) 4,888,419(4,862,291−4,914,803) 3,909,683(3,889,792− 3,930,251) Note: The number in parentheses indicates the 95% confidence interval of the mean death number or years of life lost. Both scenarios indicate considering both climate change and emission control scenarios.
Abbreviations: COPD=chronic obstructive pulmonary disease; IHD=ischemic heart disease.Table 1. Deaths and years of life lost from main types of diseases associated with ambient PM2.5 pollution in different districts in the 2010s and in different scenarios in the 2040s in China.
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