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Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis

Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
Department of Medical Education, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia
Department of Infectious Diseases, Melbourne Medical School, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
Department of Mathematics and Statistics, La Trobe University, Bundoora, VIC 3086, Australia
Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
Transport Health and Urban Design Research Lab, Melbourne School of Design, The University of Melbourne, Melbourne, VIC 3010, Australia
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(14), 7655;
Received: 26 May 2021 / Revised: 13 July 2021 / Accepted: 14 July 2021 / Published: 19 July 2021
(This article belongs to the Special Issue Advances in Environmental and Public Health)


Background: We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies. Methods: The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the heterogeneity among studies. Results: We identified 3248 records for title and abstract review, of which 309 underwent full text screening. Thirty-six studies were included, based on the inclusion criteria. Most of the studies were from China (n = 14), India (n = 6) and the USA (n = 3). PM2.5 was the most frequently reported pollutant. PM was estimated using modelling techniques (22 studies), satellite-based measures (four studies) and direct measurements (ten studies). Mortality data were sourced from country-specific mortality statistics for 17 studies, Global Burden of Disease data for 16 studies, WHO data for two studies and life tables for one study. Sixteen studies were included in the Bayesian hierarchical meta-analysis. The meta-analysis revealed that the annual estimate of premature mortality attributed to PM2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM10. Conclusion: 253 premature deaths per million population are associated with exposure to ambient PM2.5. We observed an unstable estimate for PM10, most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM10 and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM2.5 is associated with premature mortality. Given that rapid urbanization may increase this burden in the coming decades, our study highlights the urgency of implementing air pollution mitigation strategies to reduce the risk to population and planetary health.

1. Introduction

Environmental pollution is a global public health problem [1,2]. Despite various preventive strategies, air pollution continues to be a significant contributor to adverse health outcomes, particularly premature mortality [2,3]. Particulate matter (PM) is an important contributor to all air pollutants, with PM2.5 and PM10 identified as two of the key components. Of the two, PM2.5 has been reported to reach into deep tissues, such as lungs, thereby leading to the majority of health-related impacts [4,5]. In the lungs, PM2.5 corrodes the alveoli, which may lead to chronic obstructive pulmonary disease (COPD) [5,6]. PM2.5 can also lead to peripheral vascular system damage and can directly damage the myocardium leading to arrhythmias, atherosclerosis and stroke [7,8]. The effects of PM10 lead to more acute responses, such as wheeze or hyperreactive airways and bronchitis [9]. However, there is evidence that PM10 increases cardiovascular mortality [10]. Taken together, evidence indicates that PM may increase the risk of cardio-respiratory morbidity and mortality [11].
There is a growing body of literature on the role of PM in premature mortality [3,12]. Controversy surrounds this area, in part because no synthesis of the evidence has been undertaken that specifically accounts for inconsistencies among studies, especially study population differences. Hence, to date, no research has assessed the association between PM and premature mortality adjusting for the potential influence of the heterogeneity of the findings across multiple studies. This may have led to a biased estimation of health impacts due to PM exposure [13].
In the hierarchy of evidence, randomized controlled trials are the preferred research design upon which to generate evidence. Given it is difficult to apply such methods to the study of air pollution, almost all studies apply observational approaches; such studies have limitations making it difficult to draw precise inferences. Synthesizing evidence from multiple observational studies, however, can strengthen the conclusions that can be drawn. In this study we aim to systematically review the available evidence on PM and its impact on the years of life lost, measured as premature mortality. We also conduct a Bayesian hierarchical meta-analysis to account for the likely heterogeneity between the studies selected for review.

2. Methods

Medline, CINAHL and Global Health electronic databases were systematically searched (last accessed January 2020) using keywords and Boolean/phrase terms based on particulate matter and premature mortality (Table S1: Search strategy). The search was augmented from the reference lists of the included articles. The review was registered in the International Prospective Register of Systematic Reviews (PROSPERO), systematic review registry (CRD42019134760). The inclusion criteria of our systematic review were:
  • Studies that measured PM2.5 or PM10;
  • Outcome measured as premature mortality;
  • Studies based on any study design;
  • From any population group (no ethnic groups were excluded);
  • Published in English in a peer reviewed journal;
  • Available in Medline, CINAHL and Global Health electronic databases from inception to January 2020.
The exclusion criteria were:
  • Studies which assessed pollutants other than PM2.5 and PM10, or measured these pollutants in combination with other pollutants;
  • Literature reviews;
  • Conference papers, abstracts and editorials.
For the purpose of the Bayesian hierarchical meta-analysis, we also included three further selection criteria, namely, (i) Studies that showed log normality of data; (ii) Studies that did not derive PM based on satellite observations; and (iii) Studies providing point estimates with 95% confidence intervals. We excluded studies using solely satellite observations due to the uncertainties linked to satellite-based PM, namely, poor satellite coverage in specific regions, cloud contamination and year-to-year variability as such observations can substantially impact the estimates of premature mortality when compared to global models [14].
Two authors independently reviewed study titles and abstracts for detailed review of the full text (NTW and AC). All duplicates were removed after the initial search. Any disagreements were resolved by consulting with a third, senior author (MS). Studies were excluded after full-text review if they did not meet the inclusion criteria. Data extracted for analysis included the first author’s name, publication year, country, exposure estimates and the method of exposure ascertainment, outcome definitions, the method of outcome ascertainment and key results.
The working definitions for exposures was PM. PM is the particle pollutant component in the atmosphere and is a mixture of solid particles and liquid droplets that can only be seen microscopically. Based on the size of the particles, PM is categorized as PM10 and PM2.5, defined as follows:
  • PM10: inhalable particles, with diameters that are 10 micrometers and smaller; and
  • PM2.5: fine inhalable particles, with diameters that are 2.5 micrometers and smaller.
Our outcome, premature mortality, was defined as death that occurs before the average age of death in the specific population group. It is defined as potential years of life lost.
Quality of the included studies: We assessed study quality by using the Newcastle–Ottawa scale (NOS) for observational studies [15]. This scale is comprised of three elements:
Four stars are allocated to study group selection (the first element);
Two stars are allocated to comparability of the groups (the second element); and
Three stars are allocated to ascertainment of the exposure and outcome (the final element).
The NOS score ranges from 0–9 and a methodologically robust paper can achieve a total of nine stars; a perfect score. Based on the total number of stars achieved, a study was categorized as good (a total of seven or more stars), fair (five or six stars) or poor (four stars or less) quality.
Statistical analysis: We conducted a Bayesian hierarchical meta-analysis [16,17]. We conducted two analyses; a meta-analysis for PM2.5 and a meta-analysis for PM10, in which we used as a non-informative prior an improper uniform distribution over the positive real number line, followed by a heterogeneity analysis. The basic steps followed were (i) Checking for log normality of the data; (ii) Removing studies that had not achieved log normality; (iii) Transformation of log normal to the normal distribution; (iv) Meta-analysis; and (v) Converting the estimates to their original scale.
Prior to conducting the Bayesian hierarchical meta-analysis, we adjusted for differences among the baseline population characteristics of the studies included for analysis. In the original studies, the numbers exposed to PM in each country varied. To avoid considerable disparity across studies, we calculated the premature mortality rate for each respective study year by dividing the country specific number of premature deaths by the population for the same year. Log normality of the mortality rates was assumed and checked using properties of the log normal distribution (Supplementary Material S1, S2, S3). Two studies did not satisfy the properties of the log-normal distribution and were excluded from the meta-analysis [18,19]. The transformations between the corresponding log normal and normal distributions were undertaken with the usual conversion equations in conjunction with exploiting properties of the log normal distribution in order to calculate the variances of the log normally distributed mortality rates [20,21], as detailed in Supplementary Material S4&S5 (Figure S1). The meta-analysis estimates were then transformed back to their original scale.
The analysis was carried out in freeware R, version 2019 [22] using the bayesmeta package version 2019 [17] [] (accessed on 30 January, 2020). The bayesmeta package derives the posterior distributions of the synthesized mean and heterogeneity parameter and their posterior joint distribution. The code used to carry out this analysis is available in Supplementary Material S5. The forest plots that were generated from the Bayesian analysis demonstrate the log normal mortality rates with 95% credible and prediction intervals mapped to the normal distribution. Heterogeneity plots were generated to display the posterior joint density of the log normal mortality rate and heterogeneity (τ) parameters, with a darker shading area corresponding to a higher probability density.
We used a non-informative prior as opposed to an informative prior. In the absence of clear prior evidence for mortality rates, we chose this conservative option because non-informative priors have a minimal effect on the analysis [23]. Furthermore, we chose a random effect meta-analysis instead of fixed effect meta-analysis (a more conservative approach) which assumes the potential for the original study samples to arise from different populations. In Bayesian hierarchical meta-analysis, if the number of studies is less than 20, the random effect model is the analysis of choice [23].

3. Results

The systematic search revealed 3248 papers. Following the removal of duplicates, 2849 remained for title and abstract screening. Once the title and the abstracts were screened, 309 papers were available for full text review. Of those, 36 published papers met the inclusion criteria and reported estimates on premature mortality (Figure 1). Sixteen of these papers were included in the Bayesian hierarchical meta-analysis.
The 36 published papers came from various countries; six were from India [4,24,25,26,27,28], three from the USA [29,30,31], 14 from China [19,32,33,34,35,36,37,38,39,40,41,42,43,44] and one each from Korea [45], Czech Republic [46], Canada [47], France [48], Yugoslavia [49], Japan [18] and Sweden [50]. There were four global studies [1,51,52,53] and two studies from the Asian region [54,55] (Table 1 and Table 2, Figure 2). Figure 2 shows the geographic distribution of the included studies, with the exception of the global and the Asian region studies.
Although the exposure assessed was PM, the size of the particles differed among the studies. Most studies assessed PM2.5 [1,4,24,25,29,30,31,32,33,34,35,37,39,41,42,43,44,45,51,52,53,54,55,56], some measured PM10 [19,36,38,40,46], while a number of studies included both [26,27,28,48,49,50]. Only one study specifically mentioned particles between PM2.5 and PM10 [18] (Table 1) and this paper was not included in any of the meta-analyses.
The techniques used to assess exposures varied among studies. Most studies used spatial modelling, using different modelling techniques, and four studies used satellite-based measures [4,35,43,44] (Table 1 and Table 2). Ten studies directly measured PM levels [18,19,26,28,33,36,39,40,45,49] (Table 1 and Table 2).
The outcome, premature mortality, was calculated based on some existing measurement of the country specific life expectancy. For this outcome, sixteen studies used the Global Burden of Disease data [1,4,24,25,28,33,35,37,38,41,42,43,44,52,53,58] while others used life tables [26], WHO data [51,54] and country statistics [18,19,27,29,30,31,32,34,36,39,40,44,45,46,47,48,49,50] (Table 1 and Table 2). No studies were determined to be of poor quality based on the Newcastle–Ottawa scale.

3.1. Studies Not Included in the Bayesian Meta-Analyses

Twenty studies were not included in the meta-analyses. Figure 3 shows the number of publications and the area/country of origin of the studies that were not included in the Bayesian meta-analysis.
Among the studies that were not included in the Bayesian meta-analysis, two publications [27,50] reported results based on both PM10 and PM2.5, while 13 studies reported [1,4,25,34,35,39,43,44,51,52,53,54,57] only on PM2.5 and five [18,19,36,40,46] reported only on PM10. (Table 1). The presentation of results was different across the studies; however, the direction of the associations was similar, showing an increase of premature mortality.

3.2. Results of the Bayesian Meta-Analysis

The extracted premature mortality rate of the eligible studies was utilized for the meta-analysis (Table 2). Fifteen studies were included in the meta-analysis of PM2.5 and three in the meta-analysis of PM10, while two studies that investigated the outcome based on both exposures were included in the respective analyses.
The Bayesian hierarchical meta-analysis forest plots report on the stepwise analysis. This approach is hierarchical, which differs to conventional meta-analysis. The first level of the forest plot corresponds to the relevant results of the participants in the study and the second level is generated as the study participants are nested within a study and, here, we assume the sample derived is a randomly selected sample from the exposed population.
Studies included in the PM2.5 analysis were published after 2013 and represented a limited number of countries. Six studies were from China, three from USA, three from India with one study each from Canada, Korea and Yugoslavia. City specific information was available only in one study [28].
The analysis based on PM10 represented studies from France, China and two cities from India. Therefore, most evidence here came from the Asian continent.
Values in the PM2.5 forest plot indicate the log mortality rate mapped to their corresponding normal distribution values. After conversion back to the original scale, the annual estimate of premature mortality due to PM2.5 was 253 (95%CI: 90, 643) deaths per 1,000,000 population globally (Figure 4). The predicted value, overarching the sampling error of individual studies, is the expected mean value of a future study which is 269 (95%CI: 15, 3083) per 1,000,000 population.
Similarly, the transformed results of the PM10 forest plot (Figure 5) indicate that the annual estimate of premature mortality due to exposure to PM10 was 587 (95%CI: 1, 39,746) deaths per 1,000,000 population. However, when the sampling errors of individual studies were removed, the predicted mean result for a future study was 645 (95%CI: 0, 16,106) per 1,000,000 population.

3.3. Heterogeneity of the Studies

Figure 6 and Figure 7 illustrate the joint posterior density of heterogeneity τ and the effect µ (log mortality rate), for PM2.5 and PM10, respectively. The darker area on the plots indicates the area of higher probability density. Red lines represent the 50%, 90%, 95% and 99% credible intervals of the joint distribution. The blue solid line is the conditional posterior mean log mortality rate as a function of heterogeneity, with the blue dashed lines corresponding to the 95% credible interval. The green lines indicate the marginal posterior median and 95% credible intervals for both parameters.
The observed heterogeneity for the pooled studies for PM2.5 was 1.06 (95%CI: 0.23, 2.06) and for PM10 it is 1.9 (95%CI: 0.00, 10.50).
When the true heterogeneity is compared between the PM2.5 and PM10 meta-analyses, the between-study variance (true heterogeneity) was high among the studies that have assessed the outcome based on PM10.

4. Discussion

In this systematic review, we identified thirty-six studies of either good or fair quality assessing the association between ambient PM2.5 and/or PM10 and premature mortality. All studies reported a positive association. In the meta-analysis, in which we included sixteen studies, we observed that 253 premature deaths per million population are associated with exposure to ambient PM2.5. Prediction estimates indicated that the magnitude of the PM2.5—premature mortality relationship will increase in future studies. We obtained unstable estimates for PM10, most likely due to the high level of heterogeneity among studies included.
This is the first systematic review and meta-analysis conducted to assess the association between ambient PM (both PM2.5 and PM10) and premature mortality. Our findings reflect a previous meta-analysis based on 53 studies that explored the association between ambient PM2.5 and all-cause mortality, which found that a 1 μg/m3 increase in PM2.5 was associated with a significant 1.29% increase in all-age all-cause mortality [11]. Similarly, Hanigan and colleagues reported a positive association between anthropogenic PM2.5 and premature mortality in Australia [59], albeit not a meta-analysis. A recent systematic review and a meta-analysis conducted by Jie et al. [60] also found that exposure to PM2.5 and PM10 increases mortality. In addition to mortality studies, studies which assessed Disability Adjusted Life Years (DALYs) and Health Adjusted Life Years (HALYs) as outcomes have also found that PM exposure increases health burden [61]. However, the DALYs does not include years of life lost and the HALYs calculation includes both morbidity and mortality data. In this study we did not include either of these measurements as outcomes thereby enabling us to understand the impact on years of life lost due to premature mortality, which is a long-term exposure to particulate matter pollution. Not including papers with DALYs and HALYs estimates did not bias our findings given only a limited number of papers were excluded.
It is important to highlight that our assessment has only focused on ambient PM. We considered household air pollution as a separate exposure, as in the Global Burden of Disease study. However, household air pollution and ambient air pollution are interlinked exposures as each one contributes to the other. Indeed, it has been found that emissions from the use of unclean fuels for domestic energy, when compared to other emissions such as industry and road traffic, have the largest impact on premature mortality globally [62].
The biological plausibility of the association observed cannot be underestimated. With increasing industrialization and urbanization in most regions, more PM is released into the environment, which has a negative impact on the cardiovascular, cerebrovascular and respiratory systems. This, in turn, increases the risk of mortality before the expected life expectancy. Moreover, the causal relationship we found is supported by many studies. Brook et al. [63] and Pope et al. [64] reported short term changes in PM2.5 levels which lead to changes in daily mortality rates. Thurston et al. [65] also highlighted that PM2.5 increases IHD (Ischemic Heart Diseases) and mortality and reported a dose response association. Many studies, including the Harvard Six Cities study, have also found that long term exposures to PM2.5 (Dockery et al. and Pope et al.) increase mortality and that the overall reduction of PM2.5 can reduce the mortality rates, confirming its causal association.
The advantage of our study is the statistical approach used. The Bayesian hierarchical meta-analysis, compared to the conventional meta-analysis, assesses the predicted credible intervals taking the weights of the reference population rather than the individual study results. When compared to a conventional meta-analysis, Bayesian hierarchical methods utilize a prior probability distribution in assessing this. Therefore, Bayesian hierarchical random-effect models can obtain accurate pooling effects, even with a limited number of studies in the meta-analysis. Furthermore, conventional meta-analysis cannot incorporate extreme values and small studies due to the systematic difference, limiting its application to our research question [66]. In contrast to the conventional meta-analysis, Bayesian hierarchical meta-analysis can address these issues [67].
While reading this review, an important point to note is that the strategies undertaken by individual countries to reduce the emission of PM are not uniform across the globe. Therefore, our pooled estimate of premature mortality may vary according to these varying mitigation strategies. The finding of our study is a concern pointing to the urgency of implementing strategies to mitigate this growing environmental risk factor for premature mortality; the impact of ambient PM2.5 on premature mortality is remarkably high when considering the current global population and the predicted population growth in the coming decades. Although we observed an increased premature mortality for PM10, the confidence interval was extremely wide, indicating an unstable estimate. Results therefore should be interpreted cautiously. The considerable variation observed was most likely due to the heterogeneity among the studies, and future research efforts need to focus on the effects of PM10 and premature mortality.
As with all studies, there are a number of limitations. First, heterogeneity among studies may have hidden the real burden of premature mortality due to PM exposure. The studies we analyzed do not represent the global burden of premature mortality due to PM, or the urban rural disparity, as we did not have data representing all countries of the world. Indeed, most of the studies included were conducted in China and India. Although these countries account for 36% of the world’s population, they are also among the most polluted countries, so less polluted countries may be underrepresented in our study. Further, within an individual country, the available data only represents a sample of the population, which may not reflect the true impact. The majority of studies included in our study did not adjust for weather conditions or other associated conditions. The epidemiology-based exposure dose-response functions that were applied, how premature mortality was calculated, and other factors associated with life expectancy may also hide the true association. Second, we were unable to conduct a subgroup analysis, for example by region, due to the limited number of studies and lack of variation in the countries where research was conducted. None of the studies commented on causality rather than association. Third, meteorological effects on particulate matter pollution were not quantified in our analysis. Furthermore, we have excluded the satellite-based studies from our analysis.
Notwithstanding these limitations our approach, namely pooling of the available study results to obtain a summary measure and then to statistically model the reference populations of the included studies using Bayesian hierarchical meta-analysis, is meaningful for analyzing the impact of an environmental exposure(s) in contrast to analyzing a selected sample. This has enabled findings related to environmental exposures, such as PM, where the exposure cannot be confined to a sample population, and a key outcome such as premature mortality. We recommend that future systematic reviews consider this approach when collating evidence on environmental exposures and outcomes.

5. Conclusions

Existing evidence indicates a positive association between ambient PM2.5 and premature mortality, even while accounting for heterogeneity between studies. Evidence for PM10 remains inconsistent. This is one of few meta-analyses that has explored the causal association between PM and premature mortality, taking into account the heterogeneity found in the various reported studies. This study, therefore, strengthens our current knowledge of the important relationship between exposure to PM and health outcomes, highlighting the urgency to mitigate the growing exposure to air pollutants.

Supplementary Materials

The following are available online at, S1: Addressing the differences among the baseline population of the included studies; S2: Requirements for the Bayesian Hierarchical meta-analysis; S3: The statistical analysis; S4: Testing the log normal assumption; S5: Calculating the variances of the log normal distributions; Figure S1: The results of the regression testing the assumption expressed in Equation (1).

Author Contributions

Conceptualization, M.S., S.C.D. and N.T.W.; methodology, M.S., N.T.W., S.C.D., D.V. and P.T.C.; software, N.T.W., P.T.C. and D.V.; validation, D.V., P.T.C. and A.C.; formal analysis, P.T.C., N.T.W. and D.V.; investigation, M.S. and N.T.W.; data curation, N.T.W., A.C., P.T.C. and D.V.; writing—original draft preparation, N.T.W., M.S.; writing—review and editing, S.C.D., P.T.C., A.C. and S.C.D.; supervision, M.S.; project administration, M.S., S.C.D., N.T.W., P.T.C. and A.C.; funding acquisition, N.T.W. and S.C.D. All authors have read and agreed to the published version of the manuscript.


This work was supported by a grant from the Melbourne Energy Institute, The University of Melbourne, Victoria, Australia. We would also like to acknowledge Prof. Michael Brear and Dr. Robin Schofield from Melbourne Energy Institute for their support. M.S. is funded by an NHMRC Fellowship (APP1136250).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.


We would also like to acknowledge Michael Brear and Robin Schofield from the Melbourne Energy Institute for their support.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


PMParticulate Matter
PROSPEROInternational Prospective Register of Systematic Reviews
NOSNewcastle-Ottawa Scale
PRISMAPreferred Reporting Items for Systematic Reviews and Meta-Analyses
WHOWorld Health Organization
COPDChronic Obstructive Pulmonary diseases
IHDIschaemic Heart Diseases
EPAEnvironmental Protection Authority
AODAerosol Optimal depth
UCIUpper Confidence Interval
LCILower Confidence Interval
USAUnited States of America


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Figure 1. PRISMA flow chart.
Figure 1. PRISMA flow chart.
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Figure 2. Geographical distribution of the selected studies, indicating the number of publications.
Figure 2. Geographical distribution of the selected studies, indicating the number of publications.
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Figure 3. Number of publications that were not included in the meta-analysis based on the area/country.
Figure 3. Number of publications that were not included in the meta-analysis based on the area/country.
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Figure 4. Forest plot PM2.5. The values of the forest plots indicate the log mortality rate mapped to their corresponding normal distribution values.
Figure 4. Forest plot PM2.5. The values of the forest plots indicate the log mortality rate mapped to their corresponding normal distribution values.
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Figure 5. Forest plot PM10. The values of the forest plots indicate the log mortality rate mapped to their corresponding normal distribution values.
Figure 5. Forest plot PM10. The values of the forest plots indicate the log mortality rate mapped to their corresponding normal distribution values.
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Figure 6. Heterogeneity plot PM2.5.
Figure 6. Heterogeneity plot PM2.5.
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Figure 7. Heterogeneity plot PM10.
Figure 7. Heterogeneity plot PM10.
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Table 1. Studies that were not eligible for meta-analysis.
Table 1. Studies that were not eligible for meta-analysis.
Researcher, Year of the Publication
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 dataTimeEstimated premature deaths
Annual mean for 1,000,000 population
2031–204018.1 ± 4.6
2061–207010.5 ± 3.5
2091–21006.5 ± 2.6
Guttikunda et al., 2012 [27]
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 IndiaEstimated premature deaths for the year 2010 is between 7350–16,200Good
Jain et al. 2017
India [4]
Holy city Varanasi
Annual average
Measured using Satellite-retrieved AOD
Global Burden of Disease data5700 (2800; 7500) annual premature deaths were estimated due to PM2.5
(0.16% of the population)
Buleiko et al. 2017
Czech Republic [46]
PM10 annual average
Automatic and gravimetric sampling methods
Health Statistic Yearbook data for the countryYearPM10 annual average (SD)
Premature deaths: annual (SD)
T1 (Traffic, Urban, Residential)T2 (Traffic, Urban, Trade)B1 (Background, Urban, Residential)B2 (Background, Urban, Residential, Trade)
200930.13 ± 8.66
22 ± 16
33.19 ± 15.35
32 ± 21
24.43 ± 5.71
15 ± 12
34.52 ± 8.81
31 ± 14
201034.33 ± 11.52
29 ± 19
33.84 ± 17.26
48 ± 14
27.00 ± 7.57
22 ± 14
31.43 ± 9.21
24 ± 17
201130.90 ± 12.28
28 ± 19
30.33 ± 15.92
35 ± 22
26.97 ± 9.70
21 ± 17
29.58 ± 12.74
26 ± 20
201230.32 ± 8.33
27 ± 14
27.98 ± 13.03
31 ± 17
24.15 ± 4.27
13 ± 9
33.30 ± 9.04
28 ± 16
201327.29 ± 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 data1,765,820 people aged 65 years and older in China in 2010 had premature deaths related to PM2.5 exposureFair
Lu et al. 2019
China [35]
annual satellite-retrieved
Global health data exchangeFor the year 2017: 962,900Fair
Ma et al. 2016
China [36]
PM10 annual average
Directly measured
China statistical yearbook2004 to 2013, annual premature deaths attributable to
China’s outdoor air pollution ranged from 350,000 to
Nie et al. 2018 China [39]PM2.5 hourly and daily and annually
Directly measured
China Public Health and Family Planning Statistical YearbookIn 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]
Directly measured daily calculated for the year
Health statistic yearbookAir pollutantDisease causing premature deathsDose response coefficientFair
PM10Respiratory disease0.0048
Cardiovascular diseases0.0019
Xie et al. 2016
China [43]
Satellite derived analysis
Global Burden of Disease data
In total 1.25 million premature deaths due to anthropogenic PM2.5 in 2010Fair
Wang et al. 2018
China [44]
PM2.5 annual average Satellite derived analysisProvincial level data and global burden of disease dataPremature deaths attributed to PM2.5 nationwide amounted to 1.27 million in totalFair
Nawahda et al. 2013
Japan [18]
Directly monitored by the National Institute of Environmental studies
Japan Statistics Bureau2006–2009 total of 40,000 premature deaths attributed
In 2009: 8347 (95%CI: 2087, 16,695)
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 effect12,786 (3449, 20,837)
Chronic PM10 effect15 (4, 26)
Segersson et al. 2017 [50]
PM2.5 and PM10 annual mean
dispersion modelling to assess annual mean exposure
Swedish Cause of Death RegisterNumber of premature deaths:
PM2.5: 256
PM2.5–10: 54
Fang et al. 2013
Global [51]
PM2.5 modelled annually
Using AM3 design
WHO dataGlobal estimate over 21st century annually (accounts for climate change):
100,000 95%CI: (95% CI: 66,000, 130,000)
Wang et al. 2017
Global [1]
PM2.5 annually
CMAQ modelling
Global Burden of Disease dataPM2.5-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
Silva et al. 2016
Global [52]
PM2.5 Annually
Integrated exposure–response model
Global Burden of Disease data2.23 (95% CI: 1.04; 3.33) million premature mortalities/year in 2005Good
Silva et al. 2016
Global [53]
PM2.5 Annually to forecast
ACCMIP models
Global Burden of Disease data2030: 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)
Nawahda et al.
2012 [54]
South East Asia
PM2.5 annually
CMAQ modelling
WHO data2000: 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)
Shi et al. 2018 [57]
South and South East Asia
PM2.5 Annual
GEOS-Chem chemical transport model
Global Burden of Disease dataDuring 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
Table 2. Studies Included in the Bayesian Hierarchal meta-analysis.
Table 2. Studies Included in the Bayesian Hierarchal meta-analysis.
Researcher, Year of the Publication
Size of the PM
Exposure Ascertained by:
Referred Data to Calculate Premature Mortality and the Baseline Population:Results:Quality of the Study:
Upadhyay et al., 2018 [24]
PM2.5 annual average
Calculated using WRF-Chem simulation
Global Burden of Disease data and Indian census data
1.23 × 109
PM2.5 level µg m−3Number of premature deaths avoided annually if completely mitigatedGood
Transport: 3.8 ± 4.3
Industrial: 5.5 ± 2.7
Energy: 2.2 ± 2.3
92,380 (95%CI: 40,978, 140,741)
Residential: 26.2 ± 12.5378,295 (95%CI: 175,002, 575,293)
Pooled estimate: 187,400 (95%CI: 47,073;746,038) premature deaths annually if completely mitigated the effect of PM2.5 annually
Etchie et al. 2017
India [26]
Nagpur city
PM2.5 & PM10 Annual average
Directly measured
Life tables
4.65 × 106
Premature deaths in 2013 (95%CI) due to PM2.5 was 3300 (2600, 4200)
Population in Nagpur is 4,653,570
Maji et al. 2017
India [28]
Mumbai and Delhi
PM2.5 and PM10 annualDirectly measured if unavailable in some stations a conversion factor was usedGlobal Burden of Disease data
Mumbai: 2.25 × 107
Delhi: 1.82 × 107
The annual average deaths attribute to PM2.5 in Mumbai
and Delhi was 10,880 (95%CI: 5520, 16,387) and 10,900
(95%CI: 6118, 15,879).
Annual average premature deaths attributable to PM10 was around 25,006 (95%CI: 16,550; 32,346) and 32,115 (95%CI: 22,619; 39,192) for year 1991–2015 in the urban area of Mumbai and Delhi.
Fann et al. 2018
USA [29]
PM2.5 annual average
CMAQ modelling
BenMAP-CE software
(USA Environmental protection agency. Washington, DC, USA)
Using country level data
3.18 × 108
YearNumber of premature deaths and 95%CIGood
2005150,000 (100,000, 200,000)
2011124,000 (84,000, 160,000)
2014121,000 (83,000, 160,000)
Punger et al. 2013
USA [30]
PM2.5 annual average
CMAQ modelling
BenMAP Based on centre for Disease Control Data
2.95 × 108
66,000 (95%CI: 39,300; 84,500) premature deaths in 2005Good
Sun et al. 2015
USA [31]
PM2.5 annual
WRF/CMAQ modelling
BenMAP-CE software
Using country level data
2.82 × 108
103,300 (70,400; 135,700) for the year 2000
60,700 (35,000; 86,000) for the year 2050
Requia et al. 2018
Canada [47]
PM2.5 annual estimates
EPA’s MOVES model
Statistics Canada
5.19 × 105
Total premature deaths over Hamilton to be 73.10 (95%CI: 39.05; 82.11) deaths per year.Good
Kihal-Talantikite et al., 2018 [48]
PM2.5 and PM10
The ESMERALDA Atmospheric Modelling system
Paris Death Registry2007–2009, the number of attributable deaths was equal
3209 (95%CI: 1938, 3355) and 2662 (95% CI: 2859, 3553)
Han et al. 2018
Korea [45]
PM2.5 annual average
Directly measured
CMAQ method
Using population census data
5.10 × 107
In 2015 the number of premature deaths due to PM2.5: 8539 (8428; 8649)Good
Hu et al. 2018
China [32]
PM2.5 annual average
Mean exposure taken from average from 60 cities
CMAQ model
China Public Health and Family Planning Statistical Yearbook 2014
1.35 × 109
In 2013 PM2.5 related premature deaths for adults ≥30 years old is approximately 1.30 million, 95%CI: 0.69l, 1.78 millionGood
Ji et al. 2019
China [33]
Directly measuredModelled with previous data
Global Burden of Disease data
1.05 × 108
74,000 (95% confidence interval CI: 43,000, 111,000) premature deaths were attributable to PM2.5 exposure in 2013.Good
Maji et al. 2018
China [37]
Air quality monitoring network measurements
Global burden of disease data
1.37 × 109
PM2.5 in 161 cities was 652 thousand (95%CI:298, 902) thousand premature deaths in 2015Good
Maji et al. 2017
China [38]
PM2.5 and 10
Air quality monitoring network
Global Burden of disease data
1.37 × 109
Total premature deaths in China from 2014–2015 PM2.5 722,370 (95%CI: 322,716, 987,519
PM10 pollution has caused 1,491,774 (95%CI: 972,770, 1,960,303) premature deaths (age > 30) in China
Zhao et al. 2018
China [41]
PM2.5 annual average
CMAQ modelling
Global Burden of Disease Data
1.37 × 109
PM2.5 related premature deaths in 2005 amounted to 1.72 (95%CI: 1.47, 1.99) million. The marginal contribution of household fuels was estimated at 0.91 (0.72, 1.13) million, 53% (46, 60%) of the totalGood
Zhao et al. 2019
China [42]
Beijing, Tianjin, Hebei
PM2.5 meteorologically assessed
CMAQ modelling
Global Burden of Disease data
1.12 × 108
Exposure:long term PM2.5Good
COPD17.42(95%CI: 9.45, 24.40) thousand
IHD36.29(95%CI: 27.24, 48.48) thousand
Lung cancer13.53(95%CI: 5.19, 18.19) thousand
Stroke61.91(95%CI: 27.71, 79.93) thousand
Acute lower respiratory infection0.91(95%CI: 0.62, 1.14) thousand
Annual premature deaths:
Short term PM2.5 18.7 thousand
Long term PM2.5 130.1 thousand
Martinez et al. 2018
Yugoslav Republic of Macedonia [49]
PM2.5 and PM10 annual average
Directly measured
State statistical office
5.44 × 105
PM2.5: 1199 premature deaths (95%CI: 821, 1519) in the year 2012Good
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Waidyatillake, N.T.; Campbell, P.T.; Vicendese, D.; Dharmage, S.C.; Curto, A.; Stevenson, M. Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis. Int. J. Environ. Res. Public Health 2021, 18, 7655.

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Waidyatillake NT, Campbell PT, Vicendese D, Dharmage SC, Curto A, Stevenson M. Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis. International Journal of Environmental Research and Public Health. 2021; 18(14):7655.

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Waidyatillake, Nilakshi T., Patricia T. Campbell, Don Vicendese, Shyamali C. Dharmage, Ariadna Curto, and Mark Stevenson. 2021. "Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis" International Journal of Environmental Research and Public Health 18, no. 14: 7655.

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