Table 1.

Studies that were not eligible for meta-analysis.

Researcher, Year of the Publication Country	Size of the PM Exposure Ascertained by:	Referred Data to Calculate Premature Mortality:	Results:					Study Quality
Chowdhury 2018 India [25]	PM2.5 annual average Estimate up to 2100 by applying changes in PM2.5 from baseline period (2001–2005) derived from Coupled Model Inter-comparison Project 5 (CMIP5) models to the satellite-derived baseline PM2.5	Global Burden of Disease data	Time		Estimated premature deaths Annual mean for 1,000,000 population			Good
			2031–2040		18.1 ± 4.6
			2061–2070		10.5 ± 3.5
			2091–2100		6.5 ± 2.6
Guttikunda et al., 2012 [27] India Delhi and its satellite cities—Gurgaon, Noida, Greater Noida, Faridabad, and Ghaziabad	PM2.5 and PM10 Annual average Calculated using Atmospheric Transport Modelling System (ATMoS)	2010 mortality data India	Estimated premature deaths for the year 2010 is between 7350–16,200					Good
Jain et al. 2017 India [4] Holy city Varanasi	PM2.5 Annual average Measured using Satellite-retrieved AOD	Global Burden of Disease data	5700 (2800; 7500) annual premature deaths were estimated due to PM2.5 (0.16% of the population)					Fair
Buleiko et al. 2017 Czech Republic [46]	PM10 annual average Automatic and gravimetric sampling methods	Health Statistic Yearbook data for the country	Year	PM10 annual average (SD) Premature deaths: annual (SD)				Good
				T1 (Traffic, Urban, Residential)	T2 (Traffic, Urban, Trade)	B1 (Background, Urban, Residential)	B2 (Background, Urban, Residential, Trade)
			2009	30.13 ± 8.66 22 ± 16	33.19 ± 15.35 32 ± 21	24.43 ± 5.71 15 ± 12	34.52 ± 8.81 31 ± 14
			2010	34.33 ± 11.52 29 ± 19	33.84 ± 17.26 48 ± 14	27.00 ± 7.57 22 ± 14	31.43 ± 9.21 24 ± 17
			2011	30.90 ± 12.28 28 ± 19	30.33 ± 15.92 35 ± 22	26.97 ± 9.70 21 ± 17	29.58 ± 12.74 26 ± 20
			2012	30.32 ± 8.33 27 ± 14	27.98 ± 13.03 31 ± 17	24.15 ± 4.27 13 ± 9	33.30 ± 9.04 28 ± 16
			2013	27.29 ± 8.26 27 ± 11	34.87 ± 12.03 35 ± 18	22.48 ± 6.76 19 ± 7	27.13 ± 7.20 22 ± 12
Li et al. 2018 China [34]	PM2.5 annual mean GEOS-Chem chemical transport model by Satellite data	Direct follow-up data	1,765,820 people aged 65 years and older in China in 2010 had premature deaths related to PM2.5 exposure					Fair
Lu et al. 2019 China [35]	PM2.5 annual satellite-retrieved	Global health data exchange	For the year 2017: 962,900					Fair
Ma et al. 2016 China [36]	PM10 annual average Directly measured	China statistical yearbook	2004 to 2013, annual premature deaths attributable to China’s outdoor air pollution ranged from 350,000 to 520,000					Good
Nie et al. 2018 China [39]	PM2.5 hourly and daily and annually Directly measured	China Public Health and Family Planning Statistical Yearbook	In 2014, the AFs (%) for COPD, LC, IHD, and stroke were 23% (95% CI: 12, 32%), 29% (95% CI: 11, 40%), 30% (95% CI: 21, 48%), and 46% (95% CI: 17, 57%), respectively. In 2015, with the decrease of PM2.5, the AFs had fallen to 20% (95% CI: 10, 29%), 25% (95% CI: 8, 35%), 28% (95% CI: 19, 44%), and 44% (95% CI: 15, 55%).					Good
Zhao et al. 2016 China [40]	PM10 Directly measured daily calculated for the year	Health statistic yearbook	Air pollutant	Disease causing premature deaths		Dose response coefficient		Fair
			PM10	Respiratory disease		0.0048
				Cardiovascular diseases		0.0019
Xie et al. 2016 China [43]	PM2.5 Satellite derived analysis	Global Burden of Disease data 2000–2010	In total 1.25 million premature deaths due to anthropogenic PM2.5 in 2010					Fair
Wang et al. 2018 China [44]	PM2.5 annual average Satellite derived analysis	Provincial level data and global burden of disease data	Premature deaths attributed to PM2.5 nationwide amounted to 1.27 million in total					Fair
Nawahda et al. 2013 Japan [18]	PM7.5–10 Directly monitored by the National Institute of Environmental studies	Japan Statistics Bureau	2006–2009 total of 40,000 premature deaths attributed In 2009: 8347 (95%CI: 2087, 16,695)					Good
Huang et al. 2011 China [19] Pearl River	PM10 annual average Directly measured by Environmental monitoring center	Health Statistic Yearbook data 5.71 × 107			Mean (95%CI)			Good
			Acute PM10 effect		12,786 (3449, 20,837)
			Chronic PM10 effect		15 (4, 26)
Segersson et al. 2017 [50] Sweden	PM2.5 and PM10 annual mean dispersion modelling to assess annual mean exposure	Swedish Cause of Death Register	Number of premature deaths: PM2.5: 256 PM2.5–10: 54					Good
Fang et al. 2013 Global [51]	PM2.5 modelled annually Using AM3 design	WHO data	Global estimate over 21st century annually (accounts for climate change): 100,000 95%CI: (95% CI: 66,000, 130,000)					Good
Wang et al. 2017 Global [1]	PM2.5 annually CMAQ modelling	Global Burden of Disease data	PM2.5-mortalities in East Asia and South Asia increased by 21% and 85% respectively, from 866,000 and 578,000 in 1990, to 1,048,000 and 1,068,000 in 2010. PM2.5-mortalities in developed regions (i.e., Europe and high-income North America) decreased substantially by 67% and 58% respectively					Good
Silva et al. 2016 Global [52]	PM2.5 Annually Integrated exposure–response model	Global Burden of Disease data	2.23 (95% CI: 1.04; 3.33) million premature mortalities/year in 2005					Good
Silva et al. 2016 Global [53]	PM2.5 Annually to forecast ACCMIP models	Global Burden of Disease data	2030: 17,200 (95%CI: −386,000, 661,000) 2050: −1,210,000 (95%CI: −1,730,000, −835,000) 2100: −1,310,000 (95%CI: −2,040,000, −174,000)					Good
Nawahda et al. 2012 [54] South East Asia	PM2.5 annually CMAQ modelling	WHO data	2000: 237,665 (95%CI: 59, 416,475) 2005: 405,035 (95%CI: 101,259, 810,070) 2020: 313,438 (95%CI: 78,360, 626,876)					Good
Shi et al. 2018 [57] South and South East Asia	PM2.5 Annual GEOS-Chem chemical transport model	Global Burden of Disease data	During 1999–2014, the estimated total average annual premature deaths mortality due to PM2.5 exposure in SSEA reached 1,447,000 (95% CI: 9,353,00l, 2,541,100)					Good