Next Article in Journal
Smart City, Digitalization and CO2 Emissions: Evidence from 353 Cities in China
Previous Article in Journal
Institutional and Individual Effects of Greenwashing on Food Waste
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 1
10.3390/su15010224
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Health Effects of PM2.5 Exposure in China from 2004 to 2018: A Systematic Review and Meta-Analysis

by
Fangfang Ruan
1,2,* and
Xiangang Zeng
2
1
School of International Economics and Trade, Ningbo University of Finance and Economics, Ningbo 315000, China
2
School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(1), 224; https://doi.org/10.3390/su15010224
Submission received: 10 October 2022 / Revised: 12 December 2022 / Accepted: 12 December 2022 / Published: 23 December 2022
(This article belongs to the Section Air, Climate Change and Sustainability)
Browse Figures
Versions Notes

Abstract

:
In order to comprehensively evaluate the impact of fine particulate matter (PM2.5) exposure on public health in China, and to obtain a quantitative concentration–response relationship, the literature published in multiple databases from 1980 to 2019 was searched to obtain studies on the health effects of PM2.5 on the Chinese population in this paper. According to the inclusion and exclusion criteria, 67 studies were included in the research, which covered the study period from 2004 to 2018. A systematic review shows that there are 30 diseases and 46 health effect outcomes with clear concentration–response relationships with PM2.5 in China. Seven health effects were investigated by meta-analysis. For each 10 μg/m3 increase in PM2.5, the combined overall random-effects relative risk (RR) of non-accidental mortality, mortality due to cardiovascular disease, and mortality due to respiratory disease was 1.006 (95% CI = 1.004, 1.007), 1.007 (95% CI = 1.005, 1.008), and 1.008 (95% CI = 1.006, 1.010), respectively. The RR of hospital admission due to cardiovascular disease and respiratory disease was 1.006 (95% CI = 0.999, 1.014) and 1.006 (95% CI = 1.003, 1.010), respectively. In terms of outpatient visits, a 10 μg/m3 increase in PM2.5 corresponds to a 1.004 (95% CI = 1.002, 1.006) and 1.008 (95% CI = 1.005, 1.010) RR for cardiovascular disease and respiratory disease, respectively.

1. Introduction

Air pollution and its health problems have become the focus of worldwide attention. From the Global Burden of Disease (GBD) study [1,2], air pollution is the fifth-leading Level 2 risk factor for death globally after dietary risks, high systolic blood pressure, tobacco, and high fasting plasma glucose levels. About 4.9 million deaths and 147 million disability-adjusted life years (DALYs) worldwide were attributed to air pollution exposure in 2017. Of air pollutants, particulate matter (PM) affects more people than any other pollutant [3]. More than 90% of the adverse health effects caused by air pollution are caused by particulate matter, of which about 60% are caused by fine particulate matter (PM2.5). A total of 5.25% of all deaths are attributable to ambient particulate matter pollution, making it the eighth-leading risk of death globally [2]. Ambient particulate matter pollution is closely related to haze, and, among various air pollutants, it has been most strongly linked to adverse health effects [4].
In recent decades, air pollution has also become a major environmental and public health risk in China. Data show that particulate matter pollution is the fourth-leading risk factor among Level 3 causes for deaths in China after high systolic blood pressure, smoking, and high-sodium diets. Ambient particulate matter has always been the main pollutant of air pollution in China [5]. Therefore, it is a long-term and arduous task for the Chinese government to identify the health hazards caused by particulate matter pollution and take effective measures to prevent them.
In view of the harmfulness of particulate matter pollution to public health, researchers in many countries have carried out quantitative epidemiological studies on PM pollution. Through a systematic review, useful information can be effectively integrated by meta-analysis to provide data for rational decision making.
Due to different pollution statuses, population characteristics, and exposure patterns in different countries and regions, there is a large deviation and uncertainty in applying the concentration–response coefficient of particulate matter pollution in countries other than China. Establishing the concentration–response relationship between particulate matter pollution and public health in China is of great significance for the quantitative analysis of adverse health effects caused by particulate matter pollution in China.
Concentration–response functions determined by meta-analysis are well accredited in supporting epidemiological evidence of integrated information for health impact assessments [6]. However, quantitative reviews of the adverse health effects of PM pollution in the Chinese population are still limited. To date, only eight meta-analyses with pooled effect estimates for PM have been published in English [7,8,9,10,11,12,13,14]. These meta-analyses mainly focus on death, and most of them only analyzed one or several diseases, such as respiratory diseases, cardiovascular diseases, and so on. Therefore, there is clear evidence that particulate matter pollution is associated with an increase in mortality in China, but evidence of constituent-associated health effects and morbidity in China is still insufficient [9].
The purpose of this paper is to comprehensively estimate the incidence of adverse health effects in Chinese residents exposed to fine particulate matter. Although there are indoor and outdoor sources of indoor PM2.5, most indoor PM2.5 in today’s Chinese cities comes from outdoor air [15]. Therefore, the research results for outdoor PM2.5 are also applicable to indoor PM2.5. This article systematically reviews epidemiological studies on particulate matter pollution in the Chinese population published up to December 2019 and evaluates the incidence rate of adverse health effects caused by PM2.5 through a comprehensive meta-analysis. The results apply to both outdoor and indoor particulate matter, reflecting the objective association between PM2.5 and health effects.

2. Materials and Methods

2.1. Search Methods

We conducted a systematic search for studies on PM2.5 and morbidity in the Chinese population using the English language databases PubMed and Web of Science, as well as Chinese language databases, namely the China National Knowledge Infrastructure and the Wan Fang Data Knowledge Service Platform, up to December 2019. We also searched the reference lists of identified papers for additional papers. Combinations of the following keywords were used: (1) PM2.5; and (2) adverse health effects. Then, we pre-searched the International Chemical Safety Programme (IPCS), the World Health Organization (WHO), and the United States Public Medical Library (PubMed), and finally identified a further 122 health effects search terms including the following: (3) China, Chinese, Taiwan, and Hong Kong; and (4) association, associations, relationship, relation, related, associated, impact, effect, relative risk (RR), odds ratio (OR), risk evaluation, risk assessment, toxicity, and toxic.

2.2. Inclusion Criteria

We selected studies that met all of the following criteria: (1) described the relationship between short-term PM2.5 exposure and human diseases; (2) were full-text articles; (3) explicitly specified the terms ‘morbidity/mortality/incidence/emergency/hospital admission/outpatient visit ’ as the health effect outcome of the investigation; (4) had quantitative values, including RR, OR, excess risk (ER), and other values that could be converted to OR; (5) were of a specific literature type, including periodical papers, conference papers, or dissertations; and (6) were of a specific research type, including case-crossover studies, cross-sectional studies, cohort studies, or time series.
We excluded studies that: (1) were non-population studies (such as animal studies, toxicological studies, and pharmacological studies); (2) came from the same research; or (3) were patents, standards, reports, systematic reviews, meta-analysis and other literature types. References for systematic reviews and meta-analysis were used to identify other possible related studies.

2.3. Data Extraction

The systematic screening steps are summarized in Figure 1.
For each study, two reviewers independently screened the literature, extracted data, and evaluated the methodological quality according to inclusion and exclusion criteria. In case of disagreement, a third party assisted in adjudication. Only single-pollutant model results and short-time risk estimates were included in this study. After screening the title and abstract of the literature, 501 and 512 studies were included in the full-text review by the two reviewers, among which 15 were ambiguous. After the review by the third reviewer, 509 studies were finally included in the full-text review. After the full-text review, 163 and 148 studies were included in the systematic review by the two reviewers. After being reviewed by the third reviewer, 156 studies were eventually included in the systematic review.
For the meta-analysis, all risk estimates and their 95% CI were converted to RR, expressed as a standardized increment in pollutant concentration (10 μg/m3). The concentration of PM2.5 was expressed by the daily average during the study period. Since different types of fitting lag will result in different results of the study, it was necessary to record the lag pattern of each study. Single-day lag means the mortality after 0, 1, or more days with exposure to the PM2.5 concentration of the exposure day. Multiday lag means the mortality after 1 or more days with exposure to the moving average PM2.5 concentration of 2 or more days. If more than one lag pattern was recorded in the study, all lag pattern results needed to be recorded. For studies with multiple lag patterns, the mean value was used in the meta-analysis.

2.4. Quality Assessment

Using the Newcastle–Ottawa Quality Assessment Scale (NOS), we evaluated each study’s validity. The evaluation of case–control studies and cross-sectional studies included three aspects: selection method between case group and control group, comparability between case group and control group, and exposure assessment method. Additionally, the evaluation of cohort studies and time series studies included the selection of cohort, comparability, and result measurement.
Both NOS checklists are given in the form of 8 questions and are designed to help the assessor think about the validity of each study. Each question is answered by ‘yes’, ‘no’, and ‘can’t tell’, and studies were included in the meta-analysis if they obtained five or more ‘yes’ answers. All included papers were independently evaluated.

2.5. Meta-Analysis

In this paper, the risk ratios value of each health effect was used to represent the final result, for each 10 μg/m3 increase in PM2.5. Only studies that reported adjusted hazard ratios (HR), RR, and OR for the risk of health effects per increment change in PM2.5 concentrations were included. HH, RR and OR were all included in the same meta-analyses. The effect estimates of the selected studies were summarized using the inverse variance method, and the overall effect estimates were the average of the single study effect estimates, which were obtained by the inverse weighting of the study variance.
In a meta-analysis, the standard I2 test (variation in RR attributable to heterogeneity) was calculated to evaluate the statistical heterogeneity across studies [16]. We considered an I2 value ≥50% at the 10% level of significance to suggest substantial heterogeneity. If I2 exceeded 50%, the fixed-effects model was used to estimate the adverse health effects. On the contrary, the random-effects model was used for estimation. Potential publication bias was assessed by Egger’s test. All analyses were also performed using fixed-effect models as sensitivity analyses. Additionally, we considered 4 sensitivity analyses on the outcomes, excluding the study that contributed to the largest weight (the smallest standard error) to test the robustness of the findings, excluding case-cross studies, where the potential for selection bias was higher, excluding studies with special characteristics that might compromise the generalizability of findings, and using the trim and fill method to account for the publication bias.
For studies with large heterogeneity, subgroup analysis was used to explore the source of heterogeneity and the influence of covariates on the merger effect [16]. Subgroup analysis included age (<65 versus ≥65; the population aged 65 and above is generally defined as the elderly population internationally), gender (male versus female), regional divisions, (north versus south), lag patterns (single-day lags versus multiday lags), and temperature (cold seasons (from April to September) versus warm seasons (from October to March)). Random-effects metaregression was used to examine the linear tendency of the percent increase in outcome across PM2.5 concentrations. All analyses were performed in Stata 14.0 (Stata Corp LLC, College Station, TX, USA). The two-sided p-value < 0.05 was considered statistically significant, unless where otherwise specified.

3. Results

3.1. Included Studies and Areas

A total of 2392 related articles were retrieved in the preliminary survey. After screening the titles and abstracts according to the inclusion criteria, 509 full-text articles were included in the comprehensive qualification review, and 151 studies met the retrieval and screening criteria. In addition, five previously eliminated studies were included in the analysis from the published meta-analysis literature. All the studies adopted the International Classification of Diseases revision 10 (ICD-10) for the coding of the disease causes. A systematic review of 156 studies showed that there were 30 diseases and 46 health effect outcomes with clear concentration–response relationships to PM2.5 in China (Table S1). After all the data were summarized, based on the requirement that the number of studies in each health effect outcome was greater than three, 67 studies finally met the requirements of meta-analysis, involving non-accidental mortality (ICD-10: A 00-R 99), cardiovascular disease (CVD) (ICD-10: I 00-I 99), and respiratory disease (RD) (ICD-10: J 00-J 99). This paper classified emergencies as outpatient visits.
Of the 67 studies, 41 were published in English and 26 in Chinese, published from 2006 to 2019. The studies were conducted from 2004 to 2018, involving 30 cities in mainland China and Taiwan, while some studies did not specify the study cities. The study area of eight works involved two or more cities, and two works’ study areas were nationwide. Northern cities included Beijing, Harbin, Jinan, Lanzhou, Qingdao, Shenyang, Shijiazhuang, Taiyuan, Weifang, Xi’an, and Yinchuan. Southern cities included Chengdu, Dongguan, Foshan, Gaoxiong, Guangzhou, Hangzhou, Hefei, Jiangmen, Miaoli, Nanjing, Ningbo, Shanghai, Shennongjia, Shenzhen, Taipei, Xinzhu, Wuxi, Zhoushan, and Zhuhai. Figure 2 shows the regions involved in the literature included in the meta-analysis (except for the research literature whose study areas were nationwide). The research areas were mainly concentrated in coastal areas, of which Beijing, Guangdong, Zhejiang, and Shandong were the most frequently studied areas. Of the studies, there were 53 time-series studies and 14 case-cross studies. The descriptive characteristics of the 67 studies are shown in Table S2 [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83].

3.2. Meta-Analytic Summary Risks Estimates

The results of the overall meta-analysis of random effects and fixed effects are presented numerically in Table S3, along with the heterogeneity parameters and the number of studies included in each analysis. Table S3 also shows the results of the sensitivity analysis. The results of health effects are described below.

3.3. Risks in Association with Non-Accidental Mortality

There is observable publication bias among the included studies for non-accidental mortality (Egger’s p = 0.001). In the overall meta-analysis, the random-effects overall risk estimate for non-accidental mortality is statistically significantly increased (1.006, 95% CI = 1.004, 1.007). There is substantial and statistically significant heterogeneity (I2 = 93.4%). Results from the fixed-effects model are comparable. Results from sensitivity analysis show a statistically significant increase in the risk of non-accidental mortality with PM2.5, as did the fixed-effects model.
Table 1 summarizes the results of all the subgroup analyses. In the age-specific meta-analysis, the random-effects risk estimate is increased and statistically significant for those ≥65 years old, while that of those <65 years old is not statistically significant; heterogeneity remains high in both analyses. The risk estimate for ≥65 age groups (1.012, 95% CI = 1.005, 1.019) is higher than the overall risk estimate. In the gender-specific meta-analysis, heterogeneity is noted as a significant reduction, and the female group is even as low as zero. The gender-specific analysis shows statistically significantly increased risks in both gender groups from the fixed-effects model, while women (1.008, 95% CI = 1.004, 1.011) are at higher risk than men (1.005, 95% CI = 1.003, 1.006). In the region-specific meta-analysis, the results show that both regions remain highly heterogenous and have statistically significant risk estimates, while the southern cities appear to have a higher percent increase. There are 1.003 (95% CI = 1.002, 1.005) and 1.007 (95% CI = 1.005, 1.009) risk estimates in non-accidental mortality for single-day lags and multiday lags, respectively, with high heterogeneity. When it comes to temperature, the correlation between non-accidental mortality and PM2.5 is not statistically significant in the cold seasons, while it is statistically significant in the warm seasons (1.025, 95% CI = 1.008, 1.043), with reduced heterogeneity. Subgroup analysis shows that gender and temperature differences might be the sources of heterogeneity in the relationship between non-accidental mortality and PM2.5.
The average daily PM2.5 concentrations in 21 studies for non-accidental mortality have a wide range, from 30.65 μg/m3 to 173 μg/m3. The concentration–response relationship between PM2.5 concentrations and effect estimates of non-accidental mortality was examined using a metaregression model. A 0.211 p-value shows that there is no correlation between PM2.5 concentration and heterogeneity. The p-value obtained by the metaregression model with temperature as the covariate was 0.019, which further indicates that temperature is one of the sources of heterogeneity.

3.4. Risks in Association with Cardiovascular Diseases Mortality

According to Egger’s test (p = 0.009), there is obvious publication bias in the CVD mortality. In the overall meta-analysis, the random-effects overall risk estimate for CVD mortality is statistically significantly increased (1.007, 95% CI = 1.005, 1.008), with 92.3% estimated heterogeneity. The results from the fixed-effects model are comparable. Random-effects overall risk estimate remains statistically significantly increased in all sensitivity analyses for CVD mortality, while the heterogeneity remains high.
Table 2 summarizes the results of all the subgroup analyses. In the age and gender subgroups, due to the limited number of studies, the risk estimate is determined only for people over 65 and women. The results indicated that the heterogeneity of the population over 65 is reduced to zero, and the risk value (1.004, 95% CI = 1.003, 1.005) is lower than the overall risk estimate. The heterogeneity of females is reduced too, which is significant at the level of 10%. In the region-specific group, the heterogeneity of southern cities and northern cities is still high, while the risk estimate of southern cities (1.009, 95% CI = 1.006, 1.012) is higher than that of northern cities (1.005, 95% CI = 1.003, 1.007). In the pattern-specific lag, the heterogeneity of single-day lag decreased significantly. For temperature, the heterogeneity of both decreases, in which the warm season has no estimated heterogeneity, and the risk estimate of the cold seasons (1.026, 95% CI = 1.005, 1.047) is higher than that of the warm seasons (1.013, 95% CI = 1.004, 1.002). Subgroup analysis shows that age, gender, lag patterns, and temperature differences might be the sources of heterogeneity in the relationship between CVD mortality and PM2.5.
In the 32 studies for CVD mortality, the daily average PM2.5 concentration ranged from 30.65 μg/m3 to 182.2 μg/m3, and the daily average temperature ranged from 4.9 °C to 25.78 °C. Random-effects metaregression was conducted to examine the linear tendency of the percent increase in CVD mortality across PM2.5 concentrations and temperature, respectively. With p-values of 0.125 and 0.123, it seems that the two covariates are not correlated with heterogeneity.

3.5. Risks in Association with Respiratory Diseases Mortality

According to Egger’s test (p = 0.079), there is no obvious publication bias in the RD mortality. In the overall meta-analysis, the random-effects overall risk estimate for RD mortality is statistically significantly increased (1.008, 95% CI = 1.006, 1.010), with 63.3% estimated heterogeneity. Results from the fixed-effects model are comparable. All sensitivity analyses for CVD mortality are statistically significant, with the risk estimates similar to the overall risk estimate, indicating the robustness of meta-analysis for RD mortality.
Table 3 summarizes the results of all the subgroup analyses. For the group over 65, compared with the overall meta-analysis, heterogeneity is reduced, and RR (1.012, 95% CI = 1.002, 1.022) is higher. The heterogeneity of the female group (16.2%) is significantly reduced, and RR is also higher than the overall level. In the region-specific groups, the RR of southern cities (1.011, 95% CI = 1.007, 1.015) is higher than that of northern cities (1.006, 95% CI = 1.004, 1.009), while the heterogeneity of southern cities is significantly reduced. In addition, the heterogeneity of both lag patterns decreases compared with the overall level. In addition, due to the lack of research, we were unable to conduct a subgroup analysis for temperature. Subgroup analysis shows that age, gender, region, and lag pattern differences might be the sources of heterogeneity in the relationship between RD mortality and PM2.5.
In the 18 studies for RD mortality, the daily average PM2.5 concentration ranges from 31.29 μg/m3 to 118.80 μg/m3, and the daily average temperature ranges from 8.91 °C to 23.44 °C. A metaregression model was used to determine the relationship between PM2.5 concentration and its effect estimates, and temperature and its effect estimates, respectively. It seems that the PM2.5 concentrations are not correlated with heterogeneity, with 0.119 p-values. Figure 3 shows that the risk estimate for RD mortality increases with temperature (p = 0.012), suggesting that temperature was associated with heterogeneity.

3.6. Risks in Association with Cardiovascular Diseases Hospital Admissions

For CVD hospital admissions, there were only three studies [56,57,58] that met the requirements of meta-analysis in this paper, all of which were single-day lag patterns, combined with extremely high heterogeneity (99.1%). As shown in Table S3, in the random-effects model, the results are not statistically significant, while in the fixed-effects model, the results are statistically significant, with an overall risk estimate of 1.003 (95% CI 1.003, 1.003). Due to the small volume of literature, the results of the meta-analysis might have little reference significance.

3.7. Risks in Association with Respiratory Diseases Hospital Admissions

There is no observable publication bias among the included studies for RD hospital admissions (Egger’s p = 0.100). In the overall meta-analysis, the random-effects overall risk estimate for RD hospital admissions is statistically significantly increased (1.006, 95% CI = 1.003, 1.010), with 93.9% estimated heterogeneity. Results from the fixed-effects model are comparable. All sensitivity analyses for RD hospital admissions indicate the robustness of meta-analysis. Moreover, the heterogeneity (42.1%) decreases significantly after case-cross studies are excluded. Subgroup analysis and metaregression could not be performed due to insufficient literature.

3.8. Risks in Association with Cardiovascular Diseases Outpatient Visits

According to Egger’s test (p = 0.990), there is no obvious publication bias in the CVD outpatient visits. In the overall meta-analysis, the random-effects overall risk estimate for CVD outpatient visits is statistically significantly increased (1.004, 95% CI = 1.002, 1.006), with 91.7% estimated heterogeneity. The results of the sensitivity analysis and fixed-effect model are consistent with the overall meta-analysis, which meant the robustness of the meta-analysis for CVD outpatient visits.
Table 4 summarizes the results of all the subgroup analyses. For the group over 65 and the female group, there is no heterogeneity, indicating that age and gender differences might be sources of high heterogeneity for CVD outpatient visits. In the region-specific groups, the RR of southern cities (1.007, 95% CI = 1.000, 1.013) is higher than that of northern cities (1.004, 95% CI = 1.002, 1.006), while the heterogeneity of both is still high. On the other hand, there is no obvious heterogeneity in the multi-day lag patterns subgroup, indicating that the different patterns of lag might be one of the sources of heterogeneity.
In the 11 studies for CVD outpatient visits, the daily average PM2.5 concentration ranged from 37.9 μg/m3 to 121.58 μg/m3, and the daily average temperature ranged from −3.5 °C to 22.16 °C. When we examined the PM2.5 concentration effect and the temperature effect using metaregression technique, it seemed that neither of them were correlated with heterogeneity, with 0.921 and 0.740 p-values, respectively.

3.9. Risks in Association with Respiratory Diseases Outpatient Visits

According to Egger’s test (p = 0.211), there is no obvious publication bias in the RD outpatient visits. In the overall meta-analysis, the random-effects overall risk estimate for RD outpatient visits is statistically significantly increased (1.008, 95% CI = 1.005, 1.010), with 98.6% estimated heterogeneity. Results from the fixed-effects model are comparable, and sensitivity analyses supported these findings.
Table 5 summarizes the results of all the subgroup analyses. Among all the subgroup analyses, compared with the overall meta-analysis, only gender subgroup heterogeneity decreased, which indicated that gender difference might be one of the sources of high heterogeneity for RD outpatient visits. According to the RR value of subgroup analysis, the female group is higher than the male group; the southern cities are higher than the northern cities; and the multi-day period patterns were higher than the single-day lag patterns.
In the 14 studies for RD outpatient visits, the daily average PM2.5 concentration ranged from 21.8 μg/m3 to 119.8 μg/m3, and the daily average temperature ranged from −3.5 °C to 25.0 °C. Random-effects metaregression was conducted to examine the linear tendency of the percent increase in RD outpatient visits across PM2.5 concentrations and temperature, respectively. It seems that neither of them were correlated with heterogeneity, with 0.592 and 0.532 p-values, respectively.

3.10. Summary

In this systematic review and meta-analysis, we combined 156 Chinese population-based studies published from 1980 to 2019 to investigate the relationship between short-term PM2.5 exposure and public health. The previous systematic reviews only focused on certain health outcomes. Our study is the first to comprehensively review the impact of PM2.5 on the health of the Chinese population. We performed an overall Meta-analysis and estimated statistically significant risk estimates for the random effects model, including non-accidental mortality, cardiovascular disease, and respiratory disease. Multiple sensitivity analyses supported our findings and conclusions. For each 10 μg/m3 increase in PM2.5 concentration, the combined overall random-effects RR of non-accidental mortality, mortality due to cardiovascular disease, and mortality due to respiratory disease were 1.006 (95% CI = 1.004, 1.007), 1.007 (95% CI = 1.005, 1.008), and 1.008 (95% CI = 1.006, 1.010), respectively. The total RRs of hospital admissions due to cardiovascular disease and respiratory disease were 1.006 (95% CI = 0.999, 1.014) and 1.006 (95% CI = 1.003, 1.010) for a 10 μg/m3 increase in PM2.5, respectively. In terms of outpatient visits, a 10 μg/m3 increase in PM2.5 corresponded to 1.004 (95% CI = 1.002, 1.006) and 1.008 (95% CI = 1.005, 1.010) RR of cardiovascular disease and respiratory disease, respectively.

4. Discussion

4.1. Interpretation of the Results

In the overall meta-analysis, the heterogeneity of health outcomes estimates exceeded 90%, except for the RD mortality estimate. Overall, we noted significant variability in age, gender and confounder adjustment. We conducted subgroup analysis for five health outcomes in addition to RD and CVD hospital admission. The results showed that there are differences between the estimates of northern cities and southern cities, and the RR of southern cities is relatively higher than that of northern cities. This difference indicates that the risk of PM2.5 on public health in southern China is more serious than that in northern China. The previous review of the link between PM2.5 and RD mortality in China reached the same conclusion [7]. Moreover, estimates of different lag patterns also differ. The estimates of the multiday lag patterns are higher than those of the single-day lag patterns, except for CVD outpatient visits.
In the whole study, there is no statistically significant linear relationship between the observed PM2.5 health effects and concentration, thus not providing sufficient evidence for the current concentration threshold of PM2.5 in China. Although the results of the metaregression analysis are not statistically significant, the fitting results show that RR has a slight downward tendency with the increase in PM2.5, and the concentration–response curve tends to be stable at extremely high PM2.5 levels. We only found a statistically significant linear relationship between PM2.5 health effects and temperature at RD mortality by metaregression analysis.

4.2. Strengths and Limitations

To our knowledge, this study is the first meta-analysis that comprehensively examines the relationship between PM2.5 pollution and public health in China. The literature review indicates that there are 30 diseases and 46 health effect outcomes with clear concentration–response relationships with PM2.5 in China. The assessment of the health effects of PM2.5 in China also fills the gap between the previously predicted values based only on extrapolated data from western countries. It can provide information for future research and design to increase their effectiveness. We systematically assessed the relationship between PM2.5 and non-accidental mortality, cardiovascular diseases, and respiratory diseases, revealed the severity of the hazards of PM2.5 pollution, and provided a scientific basis for Chinese public health authorities to formulate relevant disease prevention and control policies. At the same time, we fully considered the influence of gender, age, region, lag pattern, and temperature on the estimation to provide a more comprehensive interpretation of the results. However, due to the lack of available studies, some health effects cannot be completed by subgroup analysis.
In the overall meta-analysis, there are only a few references included in the hospital admission of CVD RD, which may lead to the lack of reliability of the results. We only estimated the single pollutant model, without adjusting the impact of other pollutants on public health. However, PM10, NO2, and SO2 all have impacts on public health [8]. Additionally, the effects of PM2.5 might be overestimated, as our estimates are based on short-term exposure to air pollution. Our study estimated the impact of health on the general population; however, the impact may be greater in patients with pre-existing cardiovascular disease. In the process of data extraction, we also paid attention to other confounding factors, including model morphology, study type, co-pollutants, and humidity. Our study found that there might be significant differences in health effects among different genders and ages. However, due to the lack of literature, further analysis cannot be conducted, so future studies need to provide a more comprehensive analysis. For geographic areas, among the 60 single-city studies, research in the north is concentrated in Beijing (17), while that in the south is concentrated in Guangzhou (4), Shenzhen (4), and Shanghai (3). This limited number of cities cannot allow us to extrapolate our findings to a larger geographical area. In addition, most cities are densely populated megalopolises, so it remains unclear whether our results can be extrapolated to smaller cities.
Finally, this paper ignores indoor sources of PM2.5 and assumes that indoor and outdoor particulate matter has the same effect on the human body. In fact, from the perspective of source and composition, indoor and outdoor particles are not the same, because the particles will adsorb indoor SVOCS after entering the room, leading to the increase in organic compounds [84,85]. However, such inconsistency cannot be determined in the C-R or D-R relationship at present. This is the limitation of this paper and other similar papers, and it is worth further research in the future.

4.3. Studies Heterogeneity

To further understand the influence of lag patterns and regions on heterogeneity, subgroup analysis is conducted by lag patterns and cities, respectively. The results are shown in Tables S4 and S5. From the perspective of lag patterns, the heterogeneity of different lag patterns is greatly different, with some subgroup analysis results not statistically significant; it is thus impossible to determine whether the lag pattern is the source of heterogeneity. From the perspective of cities, the RR values of Beijing’s health outcomes are lower than the overall RR estimates. Additionally, the RR of CVD mortality of Guangzhou is higher than that of Beijing. This result is consistent with our conclusion. The difference between Beijing and Guangzhou also shows that regional difference is one of the sources of heterogeneity.

5. Conclusions

To sum up, although no statistically significant concentration–response tendency is found in our meta-analysis, our study confirms and quantifies the negative correlation between short-term exposure to PM2.5 and public health in China. PM2.5-associated RR might be higher in some cities in southern China. To identify the vulnerable populations and to further prevent the harmful effects of air pollution, there is an urgent need for more high-quality research to identify concentration–response correlation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15010224/s1, Table S1: Diseases and health effect outcomes; Table S2: Descriptive characteristics of the 67 studies; Table S3: Overall meta-analysis results; Table S4: Subgroup-analysis from cities; Table S5: Subgroup-analysis from lag patterns. References [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83] are cited in the supplementary materials.

Author Contributions

Conceptualization, X.Z.; methodology, F.R. All authors have read and agreed to the published version of the manuscript.

Funding

This work is funded by the Zhejiang Provincial Philosophy and Social Science Planning Project (22NDQN285YB) and the national key Research and Development of China (2017YFC0702701).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This work is supported by the Ningbo Key Research Base for Philosophy and Social Studies and the Yongjiang Social Science Youth Talent Training Plan.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Forouzanfar, M.H.; Alexander, L.; Anderson, H.R.; Bachman, V.F.; Biryukov, S.; Brauer, M.; Burnett, R.; Casey, D.; Coates, M.M.; Cohen, A. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015, 386, 2287–2323. [Google Scholar] [CrossRef] [Green Version]
  2. Stanaway, J.D.; Afshin, A.; Gakidou, E.; Lim, S.S.; Abate, D.; Abate, K.H.; Abbafati, C.; Abbasi, N.; Abbastabar, H.; Abd-Allah, F.; et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392, 1923–1994. [Google Scholar]
  3. Brook, R.D.; Rajagopalan, S.; Pope, C.A.I.; Brook, J.R.; Bhatnagar, A.; Diez-Roux, A.V.; Holguin, F.; Hong, Y.; Luepker, R.V.; Mittleman, M.A.; et al. Particulate Matter Air Pollution and Cardiovascular Disease An Update to the Scientific Statement From the American Heart Association. Circulation 2010, 121, 2331–2378. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Pope, C.A.I.; Dockery, D.W. Health effects of fine particulate air pollution: Lines that connect. J. Air Waste Manag. Assoc. 2006, 56, 709–742. [Google Scholar] [CrossRef] [PubMed]
  5. Zhou, M.; Wang, H.; Zeng, X.; Yin, P.; Zhu, J.; Chen, W.; Li, X.; Wang, L.; Wangm, L.; Liu, Y.; et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019, 394, 1145–1158. [Google Scholar] [CrossRef] [Green Version]
  6. Sheehan, M.C.; Lam, J.; Navas-Acien, A.; Chang, H.H. Ambient air pollution epidemiology systematic review and meta-analysis: A review of reporting and methods practice. Environ. Int. 2016, 92–93, 647–656. [Google Scholar] [CrossRef] [PubMed]
  7. Ren, M.; Fang, X.; Li, M.; Sun, S.; Pei, L.; Xu, Q.; Ye, X.; Cao, Y. Concentration-Response Relationship between PM2.5 and Daily Respiratory Deaths in China: A Systematic Review and Metaregression Analysis of Time-Series Studies. BioMed Res. Int. 2017, 2017, 5806185. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Zhao, L.; Liang, H.; Chen, F.; Chen, Z.; Guan, W.-J. Association between air pollution and cardiovascular mortality in China: A systematic review and meta-analysis. Oncotarget 2017, 8, 66438–66448. [Google Scholar] [CrossRef] [Green Version]
  9. Lu, F.; Xu, D.; Cheng, Y.; Dong, S.; Guo, C.; Jiang, X.; Zheng, X. Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population. Environ. Res. 2015, 136, 196–204. [Google Scholar] [CrossRef]
  10. Shang, Y.; Sun, Z.; Cao, J.; Wang, X.; Zhong, L.; Bi, X.; Li, H.; Liu, W.; Zhu, T.; Huang, W. Systematic review of Chinese studies of short-term exposure to air pollution and daily mortality. Environ. Int. 2013, 54, 100–111. [Google Scholar] [CrossRef]
  11. Lai, H.; Tsang, H.; Wong, C. Meta-analysis of adverse health effects due to air pollution in Chinese populations. BMC Public Health 2013, 13, 360. [Google Scholar] [CrossRef] [PubMed]
  12. Kan, H.-D.; Chen, B.-H.; Chen, C.-H.; Wang, B.-Y.; Fu, Q.-Y. Establishment of exposure-response functions of air particulate matter and adverse health outcomes in China and worldwide. Biomed. Environ. Sci. 2005, 18, 159–163. [Google Scholar] [PubMed]
  13. Aunan, K.; Pan, X. Exposure-response functions for health effects of ambient air pollution applicable for China—A meta-analysis. Sci. Total Environ. 2004, 329, 3–16. [Google Scholar] [CrossRef] [PubMed]
  14. Lin, H.; Wang, X.; Liu, T.; Li, X.; Xiao, J.; Zeng, W. Air Pollution and Mortality in China. Adv. Exp. Med. Biol. 2017, 1017, 103–121. [Google Scholar] [PubMed]
  15. Xiang, J.; Seto, E.; Mo, J.; Zhang, J.J.; Zhang, Y. Impacts of implementing Healthy Building guidelines for daily PM2.5 limit on premature deaths and economic losses in urban China: A population-based modeling study. Environ. Int. 2021, 147, 106342. [Google Scholar] [CrossRef] [PubMed]
  16. Zhang, T.; Dong, S.; Zhou, Z. Advanced Meta-Analysis in Stata; Fudan University Press: Shanghai, China, 2015. [Google Scholar]
  17. Chen, L.; Zhao, C.; Guan, M.; Liu, Y.; Xu, D.; Song, J. Time-series analysis of the relationship between particulate matter and mortality in Shijiazhuang. Chin. J. Dis. Control. Prev. 2018, 22, 272–277. [Google Scholar]
  18. Feng, J.; Sun, H.; Du, Y.; Zhang, X.; Di, H. Analysis on the acute effects of PM2.5 pollutants concentration on mortality of residents in Shijiazhuang. Mod. Prev. Med. 2018, 45, 603–608. [Google Scholar]
  19. Li, T.; Cui, L.; Chen, C.; Sun, Q.; Liu, Y.; Wang, Q.; Du, Y.; Du, Z.; Xu, D. Air pollutant PM 2.5 related excess mortality risk assessment in Beijing, January 2013. Dis. Surveill. 2015, 30, 668–671. [Google Scholar]
  20. Wu, H.; Tan, A.; Huang, L.; Xiao, W.; Liang, X.; Ning, T. Time-series analysis on association between atmospheric pollutants and daily mortality among residents in Zhuhai. J. Environ. Health 2017, 4, 797–800. [Google Scholar]
  21. Xiong, Y. The Characteristics of Air Pollution and Impacts on Human Health in Guangzhou; Guangxi Medical University: Nanning, China, 2016. [Google Scholar]
  22. Hu, K.; Guo, Y.; Hu, D.; Du, R.; Yang, X.; Zhong, J.; Fei, F.; Chen, F.; Chen, G.; Zhao, Q.; et al. Mortality burden attributable to PM1 in Zhejiang province, China. Environ. Int. 2018, 121, 515–522. [Google Scholar] [CrossRef]
  23. Kan, H.; London, S.J.; Chen, G.; Zhang, Y.; Song, G.; Zhao, N.; Jiang, L.; Chen, B. Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China. Environ. Int. 2007, 33, 376–384. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Li, D.; Wang, J.; Zhang, Z.; Shen, P.; Zheng, P.-W.; Jin, M.-J.; Lu, H.-C.; Lin, H.-B.; Chen, K. Association between short-term exposure to ambient air pollution and daily mortality: A time-series study in Eastern China. Environ. Sci. Pollut. Res. 2018, 25, 16135–16143. [Google Scholar] [CrossRef] [PubMed]
  25. Lin, H.; Liu, T.; Xiao, J.; Zeng, W.; Li, X.; Guo, L.; Zhang, Y.; Xu, Y.; Tao, J.; Xian, H.; et al. Mortality burden of ambient fine particulate air pollution in six Chinese cities: Results from the Pearl River Delta study. Environ. Int. 2016, 96, 91–97. [Google Scholar] [CrossRef]
  26. Zhang, J.; Liu, Y.; Cui, L.-L.; Liu, S.-Q.; Yin, X.-X.; Li, H.-C. Ambient air pollution, smog episodes and mortality in Jinan, China. Sci. Rep. 2017, 7, 11209. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Li, T.; Yan, M.; Sun, Q.; Anderson, G.B. Mortality risks from a spectrum of causes associated with wide-ranging exposure to fine particulate matter: A case-crossover study in Beijing, China. Environ. Int. 2018, 111, 52–59. [Google Scholar] [CrossRef]
  28. Ma, Y.; Chen, R.; Pan, G.; Xu, X.; Song, W.; Chen, B.; Kan, H. Fine particulate air pollution and daily mortality in Shenyang, China. Sci. Total. Environ. 2011, 409, 2473–2477. [Google Scholar] [CrossRef] [PubMed]
  29. Liang, F.; Xiao, Q.; Gu, D.; Xu, M.; Tian, L.; Guo, Q.; Wu, Z.; Pan, X.; Liu, Y. Satellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, China. Environ. Pollut. 2018, 242, 492–499. [Google Scholar] [CrossRef]
  30. Yang, C.; Peng, X.; Huang, W.; Chen, R.; Xu, Z.; Chen, B.; Kan, H. A time-stratified case-crossover study of fine particulate matter air pollution and mortality in Guangzhou, China. Int. Arch. Occup. Environ. Heal. 2012, 85, 579–585. [Google Scholar] [CrossRef]
  31. Tsai, S.-S.; Chen, C.-C.; Yang, C.-Y. Short-Term Effect of Fine Particulate Air Pollution on Daily Mortality: A Case-Crossover Study in a Tropical City, Kaohsiung, Taiwan. J. Toxicol. Environ. Heal. Part A 2014, 77, 467–477. [Google Scholar] [CrossRef]
  32. Tsai, S.-S.; Chang, C.-C.; Liou, S.-H.; Yang, C.-Y. The Effects of Fine Particulate Air Pollution on Daily Mortality: A Case-Crossover Study in a Subtropical City, Taipei, Taiwan. Int. J. Environ. Res. Public Heal. 2014, 11, 5081–5093. [Google Scholar] [CrossRef] [Green Version]
  33. Chen, C.; Zhu, P.; Lan, L.; Zhou, L.; Liu, R.; Sun, Q.; Ban, J.; Wang, W.; Xu, D.; Li, T. Short-term exposures to PM2.5 and cause-specific mortality of cardiovascular health in China. Environ. Res. 2018, 161, 188–194. [Google Scholar] [CrossRef] [PubMed]
  34. Su, J.; Du, J.; Wang, C.; Su, P.; Li, G. Relationship between air pollutants and mortality of resident population in Beijing: A time-series analysis. J. Environ. Health 2018, 5, 421–424. [Google Scholar]
  35. Li, P.; Xin, J.; Wang, Y.; Li, G.; Pan, X.; Wang, S.; Cheng, M.; Wen, T.; Wang, G.; Liu, Z. Association between particulate matter and its chemical constituents of urban air pollution and daily mortality or morbidity in Beijing City. Environ. Sci. Pollut. Res. 2015, 22, 358–368. [Google Scholar] [CrossRef]
  36. Li, G.; Xue, M.; Zeng, Q.; Cai, Y.; Pan, X.; Meng, Q. Association between fine ambient particulate matter and daily total mortality: An analysis from 160 communities of China. Sci. Total Environ. 2017, 599–600, 108–113. [Google Scholar] [CrossRef] [PubMed]
  37. Zhang, F.; Liu, X.; Zhou, L.; Yu, Y.; Wang, L.; Lu, J.; Wang, W.; Krafft, T. Spatiotemporal patterns of particulate matter (PM) and associations between PM and mortality in Shenzhen, China. BMC Public Heal. 2016, 16, 215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Dong, F.; Mo, Y.; Li, G.; Xu, M.; Ran, X. Association between ambient PM10/PM2.5 levels and population mortality of circulatorydiseases: A case-crossover study in Beijing. J. Peking Univ. Health Sci. 2013, 45, 398–404. [Google Scholar]
  39. Zheng, J.; Liu, X.; Liu, F.; Zhang, Y.; Liang, X. Correlation analysis between air pollution and risk of death from circulatory diseases of residents in Harbin. J. Environ. Occup. Med. 2018, 10, 885–891. [Google Scholar]
  40. Wang, Z.; Xu, Y.; Xiao, C.; Feng, X.; Zheng, L.; Li, F.; Huang, F. Time series analysis in ambient PM2.5 pollution and daily cardiovascular mortality in Hefei city during 2014 to 2016. J. Shandong Univ. Health Sci. 2018, 56, 48–52. [Google Scholar]
  41. Qi, A.; Liu, X.; Zhou, J.; De, X.; Li, P.; Li, L.; Zhang, Y. Effects of ambient particulate on daily cardiovascular mortality in Yinchuan: A time-series study. J. Environ. Health 2017, 34, 598–602. [Google Scholar]
  42. Zhang, Y.; Mo, Z.; Yi, J.; Wang, X.; Wang, Z. Effect of PM2.5 on daily mortality of circulatory diseases in Zhoushan City. J. Environ. Occup. Med. 2017, 34, 224–229. [Google Scholar]
  43. Cai, J.; Yu, S.; Pei, Y.; Peng, C.; Liao, Y.; Liu, N.; Ji, J.; Cheng, J. Association between Airborne Fine Particulate Matter and Residents’ Cardiovascular Diseases, Ischemic Heart Disease and Cerebral Vascular Disease Mortality in Areas with Lighter Air Pollution in China. Int. J. Environ. Res. Public Health 2018, 15, 1918. [Google Scholar] [CrossRef] [PubMed]
  44. Cao, J.; Xu, H.; Xu, Q.; Chen, B.; Kan, H. Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city. Environ. Health Perspect. 2012, 120, 373–378. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Lin, H.; Tao, J.; Du, Y.; Liu, T.; Qian, Z.; Tian, L.; Di, Q.; Rutherford, S.; Guo, L.; Zeng, W.; et al. Particle size and chemical constituents of ambient particulate pollution associated with cardiovascular mortality in Guangzhou, China. Environ. Pollut. 2016, 208, 758–766. [Google Scholar] [CrossRef] [PubMed]
  46. Lin, H.; Wang, X.; Qian, Z.; Guo, S.; Yao, Z.; Vaughn, M.G.; Dong, G.-H.; Liu, T.; Xiao, J.; Li, X.; et al. Daily exceedance concentration hours: A novel indicator to measure acute cardiovascular effects of PM2.5 in six Chinese subtropical cities. Environ. Int. 2018, 111, 117–123. [Google Scholar] [CrossRef] [PubMed]
  47. Luo, K.; Li, W.; Zhang, R.; Li, R.; Xu, Q.; Cao, Y. Ambient Fine Particulate Matter Exposure and Risk of Cardiovascular Mortality: Adjustment of the Meteorological Factors. Int. J. Environ. Res. Public Health 2016, 13, 1082. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Wang, L.; Zhang, Y.; Wei, Y.; Lu, R.; Xia, J.; Gao, X.; Liao, J.; Zeng, W. Acute effects of atmospheric particulates on mortality of cardiovascular and cerebrovascular diseases in Chengdu. Mod. Prev. Med. 2018, 45, 3504–3507. [Google Scholar]
  49. Gu, Y.; Chen, R. A case-crossover study on particulate matter air pollution and residents’ cardio-cerebrovascular mortality in Minhang District, Shanghai. J. Environ. Occup. Med. 2017, 34, 220–223. [Google Scholar]
  50. Chang, Q.; Ye, Y.; Wang, Q.; Xu, B.; Ma, X.; Ding, Z.; Zhou, L.; Chen, X. Correlation between air pollutants and cardio-cerebrovascular mortality in Nanjing. J. Environ. Occup. Med. 2017, 34, 1041–1045. [Google Scholar]
  51. Liu, X.; Wu, Y.; Fu, Y.; Zhang, F. Time-series analysis of air pollution and cardiovascular mortality in Shenzhen. Chin. J. Dis. Control. Prev. 2016, 20, 207–209. [Google Scholar]
  52. Qian, X. Ambient Air Pollution Associated with Cardiovascular Mortality in Ningbo City, 2011–2014: A Time-Series Analysis; Soochow University: Taipei, China, 2016. [Google Scholar]
  53. Mo, Z.; Fu, Q.; Zhang, L.; Lyu, D.; Mao, G.; Wu, L.; Xu, P.; Wang, Z.; Pan, X.; Chen, Z.; et al. Acute effects of air pollution on respiratory disease mortalities and outpatients in Southeastern China. Sci. Rep. 2018, 8, 3461. [Google Scholar] [CrossRef] [Green Version]
  54. Li, P.; Xin, J.; Wang, Y.; Wang, S.; Li, G.; Pan, X.; Liu, Z.; Wang, L. The acute effects of fine particles on respiratory mortality and morbidity in Beijing, 2004–2009. Environ. Sci. Pollut. Res. 2013, 20, 6433–6444. [Google Scholar] [CrossRef] [PubMed]
  55. Xue, X.; Chen, J.; Sun, B.; Zhou, B.; Li, X. Temporal trends in respiratory mortality and short-term effects of air pollutants in Shenyang, China. Environ. Sci. Pollut. Res. 2018, 25, 11468–11479. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Wu, Q.; Zheng, Y.; Piao, J.; Jiang, M.; Cui, L. Effects of air pollution on hospital admissions for cardiovascular and cerebrovascular diseases in Qingdao:a case-crossover study. J. Environ. Health 2018, 35, 283–287. [Google Scholar]
  57. Sun, Z.; An, X.; Cui, M.; Tao, Y.; Ma, X.-H.; Ye, X. The effect of PM2.5 and PM10 on cardiovascular and cerebrovascular diseases admission visitors in Beijing areas during dust weather, non-dust weather and haze pollution. China Environ. Sci. 2016, 36, 2536–2544. [Google Scholar]
  58. Liu, H.; Tian, Y.; Xiang, X.; Juan, J.; Song, J.; Cao, Y.; Huang, C.; Li, M.; Hu, Y. Ambient Particulate Matter Concentrations and Hospital Admissions in 26 of China’s Largest Cities. Epidemiology 2018, 29, 649–657. [Google Scholar] [CrossRef]
  59. Qiu, H.; Yu, H.; Wang, L.; Zhu, X.; Chen, M.; Zhou, L.; Deng, R.; Zhang, Y.; Pu, X.; Pan, J. The burden of overall and cause-specific respiratory morbidity due to ambient air pollution in Sichuan Basin, China: A multi-city time-series analysis. Environ. Res. 2018, 167, 428–436. [Google Scholar] [CrossRef]
  60. Luo, L.; Zhang, Y.; Jiang, J.; Luan, H.; Yu, C.; Nan, P.; Luo, B.; You, M. Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis. Int. J. Environ. Res. Public Heal. 2018, 15, 2160. [Google Scholar] [CrossRef]
  61. Ma, H. Case-Cross Study on the Influence of Atmospheric Fine Particulate Matter on Hospitalization of Respiratory Diseases in Qingdao City; Qingdao University: Qingdao, China, 2016. [Google Scholar]
  62. Zhu, X.; Qiu, H.; Wang, L.; Duan, Z. Risks of hospital admissions from a spectrum of causes associated with particulate matter pollution. Sci. Total Environ. 2019, 656, 90–100. [Google Scholar] [CrossRef]
  63. Guo, Y.; Jia, Y.; Pan, X.; Liu, L.; Wichmann, H.-E. The association between fine particulate air pollution and hospital emergency room visits for cardiovascular diseases in Beijing, China. Sci. Total. Environ. 2009, 407, 4826–4830. [Google Scholar] [CrossRef]
  64. Su, C.; Breitner, S.; Schneider, A.; Liu, L.; Franck, U.; Peters, A.; Pan, X. Short-term effects of fine particulate air pollution on cardiovascular hospital emergency room visits: A time-series study in Beijing, China. Int. Arch. Occup. Environ. Heal. 2016, 89, 641–657. [Google Scholar] [CrossRef]
  65. Xu, Q.; Wang, S.; Guo, Y.; Wang, C.; Huang, F.; Li, X.; Gao, Q.; Wu, L.; Tao, L.; Guo, J.; et al. Acute exposure to fine particulate matter and cardiovascular hospital emergency room visits in Beijing, China. Environ. Pollut. 2017, 220, 317–327. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Zheng, P.-W.; Shen, P.; Ye, Z.-H.; Zhang, Z.-Y.; Chai, P.-F.; Li, D.; Jin, M.-J.; Tang, M.-L.; Lu, H.-C.; Lin, H.-B.; et al. Acute effect of fine and coarse particular matter on cardiovascular visits in Ningbo, China. Environ. Sci. Pollut. Res. 2018, 25, 33548–33555. [Google Scholar] [CrossRef] [PubMed]
  67. Yang, H.-C.; Chang, S.-H.; Lu, R.; Liou, D.-M. The effect of particulate matter size on cardiovascular health in Taipei Basin, Taiwan. Comput. Methods Programs Biomed. 2016, 137, 261–268. [Google Scholar] [CrossRef] [PubMed]
  68. Wang, Z.; Zhao, J.; Niu, Z.; Qi, P. Influence of air pollution on the outpatient volume of cardiovascular and cerebrovascular diseases using the Poisson generalized additive models in Weifang. Chin. J. Health Stat. 2017, 34, 232–235. [Google Scholar]
  69. Liu, C.; Liu, Y.; Zhou, Y.; Feng, A.; Wang, C.; Shi, T. Short-term effect of relatively low level air pollution on outpatient visit in Shennongjia, China. Environ. Pollut. 2019, 245, 419–426. [Google Scholar] [CrossRef]
  70. Li, G.; Lan, H.; Liu, Z.; Rui, T.; Lu, J.; Bian, L.; Wang, Y.; Wang, S.; Zhang, H.; Bian, Y.; et al. The Association between Short-term Exposure to Fine Particulate Matter and Outpatient Visit in Beijing, China. Iran. J. Public Heal. 2017, 46, 1486–1494. [Google Scholar]
  71. Liang, F.; Tian, L.; Guo, Q.; Westerdahl, D.; Liu, Y.; Jin, X.; Li, G.; Pan, X. Associations of PM2.5 and Black Carbon with Hospital Emergency Room Visits during Heavy Haze Events: A Case Study in Beijing, China. Int. J. Environ. Res. Public Heal. 2017, 14, 725. [Google Scholar] [CrossRef] [Green Version]
  72. Chen, M. Research the Time Series Analysis the Effect of PM2.5 Pollution on Respiration and Circulation System in Winter in Beijing; Hebei Medical University: Shijiazhuang, China, 2016. [Google Scholar]
  73. Zhang, W.; Zhen, G.; Chen, D.; Shi, J.; Wu, D.; Zhang, S. The impact of air pollutants on the outpatient visits of respiratory diseases in Shunyi District, Beijing. Chin. J. Health Stat. 2017, 34, 275–279. [Google Scholar]
  74. Ma, G.; Zou, B.; Xu, Z.; Lu, J. Association of respiratory diseases outpatient visits and air pollution in a district, Guangzhou: A time-series study. J. Environ. Health 2012, 29, 526–528. [Google Scholar]
  75. Liu, P. Effects of Air Pollution on Hospital Emergency Room Visits for Respiratory Diseases and Assessment of Residents’ Willingness- to-Pay for Good Air Quality; Shandong University: Jinan, China, 2016. [Google Scholar]
  76. Peng, C.Q.; Cai, J.F.; Yu, S.Y.; Cao, Z.J.; Liao, Y.X.; Liu, N.; He, L.; Zhang, L.; Zheng, J.; Shi, X.M.; et al. Impact of PM2.5 on daily outpatient numbers for respiratory diseases in Shenzhen, China. Zhonghua yu fang yi xue za zhi [Chinese J. Prev. Med.] 2016, 50, 874–879. [Google Scholar]
  77. You, Y.; Zhu, J.; Zhang, X.; Zhang, X.; Zhou, W. Impact of air pollution on the outpatient visits of adult internal medicine in Wuxi City: A time series study. J. Shandong Univ. Health Sci. 2018, 56, 53–60. [Google Scholar]
  78. Zhao, Y.; Wang, S.; Lang, L.; Huang, C.; Ma, W.; Lin, H. Ambient fine and coarse particulate matter pollution and respiratory morbidity in Dongguan, China. Environ. Pollut. 2017, 222, 126–131. [Google Scholar] [CrossRef] [PubMed]
  79. Wang, Y.; Zu, Y.; Huang, L.; Zhang, H.; Wang, C.; Hu, J. Associations between daily outpatient visits for respiratory diseases and ambient fine particulate matter and ozone levels in Shanghai, China. Environ. Pollut. 2018, 240, 754–763. [Google Scholar] [CrossRef] [PubMed]
  80. Chai, G.; He, H.; Sha, Y.; Zhai, G.; Zong, S. Effect of PM2.5 on daily outpatient visits for respiratory diseases in Lanzhou, China. Sci. Total. Environ. 2019, 649, 1563–1572. [Google Scholar] [CrossRef] [PubMed]
  81. Xu, Q.; Li, X.; Wang, S.; Wang, C.; Huang, F.; Gao, Q.; Wu, L.; Tao, L.; Guo, J.; Wang, W.; et al. Fine Particulate Air Pollution and Hospital Emergency Room Visits for Respiratory Disease in Urban Areas in Beijing, China, in 2013. PLoS ONE 2016, 11, e0153099. [Google Scholar] [CrossRef]
  82. Wang, S.; Li, Y.; Niu, A.; Liu, Y.; Su, L.; Song, W.; Liu, J.; Liu, Y.; Li, H. The impact of outdoor air pollutants on outpatient visits for respiratory diseases during 2012–2016 in Jinan, China. Respir. Res. 2018, 19, 246–253. [Google Scholar] [CrossRef] [PubMed]
  83. Hwang, S.; Lin, Y.; Guo, S.; Chi, M.-C.; Chou, C.-T.; Lin, C.-M. Emergency room visits for respiratory diseases associated with ambient fine particulate matter in Taiwan in 2012: A population-based study. Atmos. Pollut. Res. 2017, 8, 465–473. [Google Scholar] [CrossRef]
  84. Cao, J.; Mo, J.; Sun, Z.; Zhang, Y. Indoor particle age, a new concept for improving the accuracy of estimating indoor airborne SVOC concentrations, and applications. Build. Environ. 2018, 136, 88–97. [Google Scholar] [CrossRef]
  85. Liu, C.; Shi, S.; Weschler, C.; Zhao, B.; Zhang, Y. Analysis of the Dynamic Interaction Between SVOCs and Airborne Particles. Aerosol Sci. Technol. 2013, 47, 125–136. [Google Scholar] [CrossRef]
Sustainability 15 00224 g001 550
Figure 1. Systematic screening process for literature review.
Figure 1. Systematic screening process for literature review.
Sustainability 15 00224 g001
Sustainability 15 00224 g002 550
Figure 2. The study areas involved in the meta-analysis and the number of studies.
Figure 2. The study areas involved in the meta-analysis and the number of studies.
Sustainability 15 00224 g002
Sustainability 15 00224 g003 550
Figure 3. Relationships between daily mean temperature and percent increase in respiratory mortality.
Figure 3. Relationships between daily mean temperature and percent increase in respiratory mortality.
Sustainability 15 00224 g003
Table
Table 1. Subgroup-analysis of non-accidental mortality.
Table 1. Subgroup-analysis of non-accidental mortality.
SubgroupRR (95% CI)I2/p
All1.006 (1.004, 1.007) a93.4% and 0.000
≥65 years old1.012 (1.005, 1.019) a96.0% and 0.001
<65 years old0.998 (0.986, 1.010) a80.6% and 0.747
Male1.005 (1.003, 1.006) b11.8% and 0.000
Female1.008 (1.004, 1.011) b0.0% and 0.000
Northern cities1.004 (1.002, 1.006) a89.5% and 0.000
Southern cities1.008 (1.005, 1.012) a91.3% and 0.000
Single-day lags1.003 (1.002, 1.005) a88.6% and 0.000
Multiday lags1.007 (1.005, 1.009) a90.0% and 0.000
Cold seasons1.041 (0.990, 1.094) a96.2% and 0.118
Warm seasons1.025 (1.008, 1.043) a70.6% and 0.004
a Random-effects model was used. b Fixed-effects model was used.
Table
Table 2. Subgroup-analysis of cardiovascular mortality.
Table 2. Subgroup-analysis of cardiovascular mortality.
SubgroupRR (95% CI)I2/p
All1.007 (1.005, 1.008) a92.3% and 0.000
≥65 years old1.004 (1.003, 1.005) b0.0% and 0.000
<65 years old--
Male--
Female1.007 (1.000, 1.014) a67.6% and 0.061
Northern cities1.005 (1.003, 1.007) a89.3% and 0.000
Southern cities1.009 (1.006, 1.012) a90.5% and 0.000
Single-day lags1.004 (1.003, 1.005) a63.8% and 0.000
Multiday lags1.008 (1.005, 1.012) a95.9% and 0.000
Cold seasons1.026 (1.005, 1.047) a79.7% and 0.000
Warm seasons1.013 (1.004, 1.002) b0.0% and 0.003
a Random-effects model was used. b Fixed-effects model was used.
Table
Table 3. Subgroup analysis of respiratory mortality.
Table 3. Subgroup analysis of respiratory mortality.
SubgroupRR (95% CI)I2/p
All1.008 (1.006, 1.010) a63.3% and 0.000
≥65 years old1.012 (1.002, 1.022) b45.2% and 0.016
<65 years old--
Male--
Female1.011 (1.003, 1.019) b16.2% and 0.007
Northern cities1.006 (1.004, 1.009) a70.9% and 0.000
Southern cities1.011 (1.007, 1.015) b32.2% and 0.000
Single-day lags1.004 (1.002, 1.005) b35.8% and 0.000
Multiday lags1.009 (1.007, 1.011) a53.3% and 0.000
Cold seasons--
Warm seasons--
a Random-effects model was used. b Fixed-effects model was used.
Table
Table 4. Subgroup analysis of cardiovascular outpatient visit.
Table 4. Subgroup analysis of cardiovascular outpatient visit.
SubgroupRR (95% CI)I2/p
All1.004 (1.002, 1.006) a91.7% and 0.000
≥65 years old1.009 (1.004,1.014) b0.0% and 0.001
<65 years old--
Male--
Female1.008 (1.005, 1.012) b0.0% and 0.000
Northern cities1.004 (1.002, 1.006) a94.4% and 0.000
Southern cities1.007 (1.000, 1.013) a69.5% and 0.000
Single-day lags1.004 (1.002, 1.006) a93.1% and 0.000
Multiday lags1.003 (1.001, 1.004) b4.3% and 0.001
a Random-effects model was used. b Fixed-effects model was used.
Table
Table 5. Subgroup-analysis of respiratory diseases outpatient visits.
Table 5. Subgroup-analysis of respiratory diseases outpatient visits.
SubgroupRR (95% CI)I2/p
All1.008 (1.005, 1.010) a98.6% and 0.000
Male1.005 (1.001, 1.009) a62.9% and 0.009
Female1.009 (1.004, 1.015) a79.3% and 0.001
Northern cities1.004 (1.003, 1.005) a93.0% and 0.000
Southern cities1.013 (1.005, 1.021) a98.6% and 0.002
Single-day lags1.006 (1.002, 1.010) a99.1% and 0.003
Multiday lags1.011 (1.007, 1.015) a91.7% and 0.000
a Random-effects model was used.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ruan, F.; Zeng, X. Health Effects of PM2.5 Exposure in China from 2004 to 2018: A Systematic Review and Meta-Analysis. Sustainability 2023, 15, 224. https://doi.org/10.3390/su15010224

AMA Style

Ruan F, Zeng X. Health Effects of PM2.5 Exposure in China from 2004 to 2018: A Systematic Review and Meta-Analysis. Sustainability. 2023; 15(1):224. https://doi.org/10.3390/su15010224

Chicago/Turabian Style

Ruan, Fangfang, and Xiangang Zeng. 2023. "Health Effects of PM2.5 Exposure in China from 2004 to 2018: A Systematic Review and Meta-Analysis" Sustainability 15, no. 1: 224. https://doi.org/10.3390/su15010224

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop