Association between PM_2.5 exposure and blood pressure among young adults in Asia: systematic review and meta-analysis

Abstract

Background

Ambient air pollution, particularly fine particulate matter (PM_2.5) and hypertension, are compounding public health challenges in low- and middle-income countries, contributing millions of premature deaths annually. Although several studies have explored the relationship between ambient air pollution and blood pressure, evidence remains limited for young adults. We performed a systematic review and meta-analysis to investigate the magnitude of associations between PM_2.5 exposure and blood pressure among young adults residing in Asia.

Methods

PubMed, Embase, and Scopus were searched for studies published up to December 2024. Eligible studies reported associations between PM_2.5 and blood pressure in populations aged 18–45 years. Data were extracted on study characteristics and effect estimates. Pooled mean changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP) per 10 µg/m³ increase in PM_2.5 were calculated using a random-effects model. Subgroup and sensitivity analyses were performed to explore potential sources of heterogeneity and study influence.

Results

Of 177 articles screened, 11 met the inclusion criteria. Overall, each 10 µg/m³ increase in PM_2.5 was associated with a 1.13 mmHg rise in SBP (95% CI: 0.08–2.19) and a 0.38 mmHg rise in DBP (95% CI: − 0.18 to 0.94). Subgroup analysis indicated that long-term exposure was significantly associated with elevated DBP, suggesting a cumulative effect of prolonged PM_2.5 exposure.

Conclusion

Both short-term and long-term PM_2.5 exposure was positively associated with blood pressure among young adults. These findings highlight novel evidence for overlooked population in previous reviews and underscore the need for targeted interventions and stricter air quality standards to protect cardiovascular health. As the evidence is primarily applicable to East Asian contexts, further research across other Asian regions is needed to establish broader generalizability.

Trial registration

PROSPERO (Registration DOI: PROSPERO 2025 CRD42025642716).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-026-26488-0.

Introduction

Non communicable diseases (NCDs) have emerged as global public health concerns, accounting for approximately 74% of global deaths each year, with low- and middle-income countries experiencing 77% of all NCD-related deaths [1]. Among NCDs, hypertension is a major risk factor for cardiovascular diseases, affecting 1.3 billion people worldwide [1]. Concurrently, ambient air pollution has become global environmental risk factor which is responsible for 40–60% of global premature deaths related to cardiovascular diseases, with approximately half of these deaths linked to PM_2.5 (particulate matter ≤ 2.5 μm in diameter) exposure [2–4]. Evidence has demonstrated that both short-term and long-term exposure to PM_2.5 can result in hypertension [5–7].

A systematic review and meta-analysis by Liang et al. [8] examined the effects of PM_2.5 exposure on blood pressure across 22 studies and found a positive association, with an increment of 1.393 mmHg (95% CI: 0.874;1.912) for Systolic Blood Pressure (SBP) and 0.895 mmHg (95% CI: 0.49;1.299) for Diastolic Blood Pressure (DBP) per 10 µg/m³ elevation in PM_2.5 levels [8]. As an updated systematic review and meta-analysis, Niu et al. (2022) reviewed the association between long-term exposure to ambient particulate matter and blood pressure in 41 studies, reporting significant associations between PM_2.5 with SBP and DBP [9]. However, the majority of the previous reviews focused on general populations at a global level, with most of the evidence coming from high-income countries.

Regarding the increasing prevalence of hypertension among young adults, research indicates that 1 in 8 individuals aged 20–40 years worldwide has hypertension, a figure expected to rise due to poor lifestyle behaviours and lowering of the hypertension diagnosis threshold [10]. In addition, the population attributable fraction for cardiovascular events from raised blood pressure in young adults has been shown to be higher than that observed in older adults, reinforcing the disproportionate long-term impact of early onset hypertension [11, 12]. In mainland China, a state-of-the-art review study observed a steady increase in hypertension rates among adults aged 18 years and above, with crude prevalence ranging from 18.0% to 44.7% between 2012 and 2015 [13].

Although hypertension in young adults significantly increases the likelihood of cardiovascular events in later years, many young individuals remain underdiagnosed and undertreated [10]. Risk prediction models commonly applied in hypertension guidelines to estimate 10-year atherosclerotic cardiovascular disease (ASCVD) risk have not been validated in young adults’ populations, and high-quality evidence supporting the early initiation of antihypertensive therapy in this age group remains limited [14–18]. Meanwhile, evidence indicates that ambient PM_2.5 levels in Asia have risen rapidly [19, 20]. Young adults who are living in these regions may therefore experience higher exposure doses, with early life vascular changes potentially exerting long term effects on cardiovascular health. Prolonged, unmitigated exposure compounded by limited clinical engagement places young adults in high polluted region at particular risk, reflecting the pressing need for tailored preventive and intervention strategies to mitigate the adverse effects of air pollution. This systematic review and meta-analysis aim to synthesize available evidence on the association between short term and long term PM_2.5 exposure and blood pressure among young adults in Asia.

Materials and methods

Search strategy

Literature was searched in three databases-PubMed, Embase, and Scopus, with published date until December 2024. The search strategy was based on pairwise combination of key words concerning fine particulate matter (PM_2.5), blood pressure (systolic pressure, diastolic pressure, pulse pressure, arterial pressure), Young adults (Young adult, Young people, Teenagers, Teens, Adolescent, Adolescence), Asia (Japan, Korea, Macao, Russia, China, Hong Kong, Mongolia, Taiwan, Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka, Borneo, Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka). (Supplementary Table 1)

Adolescent‑related search terms (e.g., “teenagers,” “adolescence”) were included to maximize sensitivity, as individuals aged 18–20 years are frequently indexed under these descriptors despite falling within our target age range of 18–45 years. In contrast, terms such as “university students” or “college students” were not explicitly included, since these populations are typically captured under broader age‑related descriptors of young adults. Importantly, studies conducted in the general population that reported age‑stratified results for young adults were included whenever subgroup data were available, ensuring that both dedicated young adult cohorts and age specific analysis within population were represented.

Study selection

Two independent reviewers (SWM and BZ) conducted an initial evaluation by screening titles and abstracts and then read the full text of the potentially eligible studies. The reviewers compared the results and resolved disagreements by consensus with the help of a third reviewer (HC). The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020) guidelines for reporting systematic reviews [21]. The protocol is registered on PROSPERO (Registration DOI: PROSPERO 2025 CRD42025642716).

Inclusion and exclusion criteria

Inclusion criteria

Longitudinal (cohort, panel) or cross-sectional studies report the association of PM_2.5 exposure to blood pressure and describe the effect estimate (β) and 95% CI (or sufficient data to calculate these estimates). The target population is young adults (aged 18–45 years), and the forementioned studies identifying the young adults will be included.

Exclusion criteria

Cross-over studies, randomized studies, studies including children and older adults but no age stratified results, studies evaluating indoor or household pollution, and studies those not reporting the primary outcome chosen for this review-SBP and DBP.

These exclusions were applied to ensure methodological consistency and to maintain the focus of this review on population level associations between ambient PM_2.5 exposure and blood pressure in young adults within real world settings. Experimental designs such as crossover or randomized trials typically assess short term physiological responses under controlled conditions, often with limited sample sizes and brief exposure windows. Studies focused primarily on indoor or household PM_2.5 were also excluded, as these exposures arise from highly context-specific sources (e.g., cooking fuels, heating) and do not reflect regional ambient air quality. In contrast, personal-monitoring studies included in this review primarily captured individual variation in ambient PM_2.5 exposure, with indoor exposure only as part of daily background exposure rather than the main source.

Data extraction and quality

Data were extracted and summarized by two reviewers (SWM and HC) from all eligible studies. Information recorded from each study included authors, publication year, study design, study location, sample size, participants characteristics, particulate matter, and outcome. The two reviewers compared and cross-checked the extracted data, and conflicts were adjudicated by discussion and consensus.

Quality assessment

The quality of the studies was assessed by two independent reviewers (SWM and BZ) using the New Castle Ottawa Scale (NOS) for cohort [22] and an adapted version of the scale was used for cross-sectional studies [23]. Studies scored higher than or equal to 6 were regarded as high quality, while those scored less than 6 were regarded as low quality [24]. If there was no consensus on an item, the third reviewer (HC) was responsible for making a final decision.

Data analysis and publication bias

For the meta-analysis, effect estimates used continuous data (SBP and DBP), and we used the β values with corresponding 95% CI. R Studio version 4.4.1 was used for the meta-analysis. As the studies reported the effect estimates changes in different increments of fine particulate matter, we converted them into a common exposure unit increment of 10 µg/m³ by using the following formula:

Pooled estimates were calculated using random-effects models. Heterogeneity was assessed with the I² statistic, where values of 0–40% indicate low heterogeneity, 30–60% moderate, 50–90% substantial, and 75–100% considerable heterogeneity [25].

Publication bias was assessed using funnel plot visualization, Begg’s test, and Egger’s test. To explore potential sources of heterogeneity, subgroup analyses were conducted based on exposure duration, exposure measurement, population type, sample size, and study design. Sensitivity analyses were then performed using a leave-one-out approach, in which each study was omitted sequentially to evaluate its influence on the overall pooled estimates.

For exposure assessment, durations within 30 days were classified as short-term, while those longer than 6 months were regarded as long-term. To ensure consistency across analyses, crude estimates were used for studies that reported both crude and adjusted blood pressure (BP) changes, as most studies applied different sets of covariates in their adjusted models [26, 27]. Given the wide variation in sample sizes, studies were categorized into four groups: small (< 100 participants), medium (101–1,000), large (1,001–100,000), and very large (> 100,000).

Results

Literature search and characteristics of the included studies

Study selection

The study selection process is illustrated in the PRISMA flow diagram (Fig. 1). A total of 177 records were identified from three databases (PubMed, Embase, and Scopus). After removing 97 duplicates, 80 unique records remained for screening. Following title and abstract review, 56 records were excluded. Of the 33 full-text articles assessed for eligibility, 11 met the inclusion criteria and were included in the systematic review and meta-analysis [26–36].

Fig. 1.

PRISMA flow diagram

Characteristics of the included studies

The general characteristics of the 11 studies are summarized in Table 1. Of these, 8 studies employed a longitudinal study design [26, 28–32, 35, 36] while the remaining 3 used a cross-sectional design [27, 33, 34]. The sample size across individual studies ranged from 39 [32] to 2,424,216 [26] for longitudinal studies, and from 838 [34] to 39,348,119 [33] for cross-sectional studies. The studies were conducted predominantly in mainland China [26–28, 30–36], with one from Taiwan and Hong Kong [29]. Most of the included studies focused on university students [27, 30–32, 34–36], while three studies examined the general population [26, 29, 33] and one study investigated general workers [28]. 7 out of 11 studies emphasized on short term effects of PM_2.5 on blood pressure [28, 30–32, 34–36], 4 reported long-term effects [26, 27, 29, 33]. Ambient PM_2.5 exposure data were utilized in 9 studies [26, 27, 29, 31–36], while two studies employed personal PM_2.5 exposure measurements [28, 30]. Most studies assessed blood pressure by using non-ambulatory methods [26–29, 31–36], with just one study performing ambulatory blood pressure measurement [30]. All included studies were rated as high quality based on the Newcastle–Ottawa Scale (NOS). One study scored nine [26], five studies scored eight [28–31, 35], three studies scored seven [27, 32, 36], and two studies scored six [33, 34] (Supplementary Tables 3–4).

Table 1.

Characteristics of the included studies

Author	Study design	Location	Population type	Sample size	PM 2.5 exposure duration	PM 2.5 exposure measurement	BP measurement	Mean PM 2.5 exposure level (µg/m³)	Effect estimates for BP (β)	Quality assessment (New-castle Ottawa)
Baccarelli et al.,2011 [28]	Longitudinal	Mainland China	Workers	120	Short term (8 h)	Personal	Non ambulatory	Office worker = 94.6 ± 64.9 Truck drivers = 126.8 ± 68.8	SBP= −1.01 (−0.18;0.16) DBP = 0.04 (−0.11; 0.19)	High
Ren et al., 2019 [30]	Longitudinal	Mainland China	University students	41	Short term (Single day)	Personal	Ambulatory	Personal = 60.30 ± 52.14	SBP= −0.54 (−1.08; 0.00) DBP=−0.22 (−0.59;0.15)	High
Wang et al., 2022 [31]	Longitudinal	Mainland China	University students	47	Short term (2 days)	Ambient	Non ambulatory	81.3 ± 26.6	SBP = 0.04 (0.01;0.07) DBP = 0.06 (0.02; 0.09)	High
Wu et al., 2013 [32]	Longitudinal	Mainland China	University students	39	Short term (3days)	Ambient	Non ambulatory	Suburban = 82.0 ± 46.6 Urban (1) = 78.1 ± 72.5 Urban (2) = 59.9 ± 40.3	SBP = 0.21 (0.03;0.39)	High
Ye et al., 2022 [34]	Cross- sectional	Mainland China	University students	838	Short term (Single day)	Ambient	Non ambulatory	27.87 ± 0.32 (Median ± SE)	SBP = 1.25 (0.81;1.69) DBP = 1.28 (0.35; 2.21)	High
Yuan et al., 2023 [35]	Longitudinal	Mainland China	University students	40	Short term (2 days)	Ambient	Non ambulatory	30 ± 15.04	SBP = 3.61 (1.81;5.41) DBP = 3.25 (2.01;4.49)	High
Song et al., 2022 [36]	Longitudinal	Mainland China	University students	61	Short term (Single day)	Ambient	Non ambulatory	150.6 ± 70.00	DBP= −1.04 (−1.86; −0.22)	High
Guo et al., 2023 [29]	Longitudinal	Taiwan, Hongkong	General population	4,272	Long term (2 years)	Ambient	Non ambulatory	72.7 ± 35.1	SBP = 2.57 (0.25;4.89)	High
Wang et al.,2020 [26]	Longitudinal	Mainland China	General population	2,424,216	Long term (1 year)	Ambient	Non ambulatory	61.1 (Median)	SBP = 0.21 (0.19;0.23) DBP = 0.12 (0.11;0.13)	High
Wu et al., 2023 [27]	Cross-sectional	Mainland China	University students	16,242	Long term (6years)	Ambient	Non ambulatory	46.36 (min:4.67; max 103.95)	SBP = 4.68 (3.83;5.53) DBP = 0.71 (0.19;1.22)	High
Xie et al., 2018 [33]	Cross sectional	Mainland China	General population	39,348,119	Long term (3 years)	Ambient	Non ambulatory	47.0 ± 17.0	SBP = 0.57 (0.56;0.57) DBP = 0.38 (0.38;0.39)	High

Association between PM 2.5 exposure and blood pressure

Overall analysis (random effect model)

A total of 11 studies met the inclusion criteria for the meta-analysis. Of these, 10 reported associations between PM_2.5 exposure and SBP and 10 reported associations with DBP, with several studies contributing to both outcomes. In the overall analysis, exposure to PM_2.5 was associated with an increase in SBP, with a pooled effect estimate of β = 1.13 mmHg per 10 µg/m³ PM_2.5 (95% CI: 0.08, 2.19) and extremely high heterogeneity (I² = 99.6%) (Fig. 2A). For DBP, the pooled effect estimate was β = 0.38 mmHg per 10 µg/m³ (95% CI: −0.18, 0.94), also with high heterogeneity (I² = 99.7%) (Fig. 2B). Given the considerable heterogeneity observed for both outcomes, subgroup analyses were conducted to explore potential sources of variability, including exposure duration, sample size, and population.

Fig. 2.

Forest plot of studies reporting the association between a 10 µg/m³ increase in PM_2.5 exposure and blood pressure outcomes, including SBP (A) and DBP (B)

Subgroup analysis

Sample size subgroup analysis

For SBP, studies with large sample sizes showed the strongest association, with a pooled increase of 3.91 mmHg per 10 µg/m³ PM_2.5 (95% CI: 1.93–5.90) and moderate heterogeneity (I² = 64.2%). Very large studies reported a smaller but significant increase of 0.39 mmHg (95% CI: 0.04–0.74), though heterogeneity remained extremely high (I² = 99.9%). Studies with small (4 studies) and medium (2 studies) sample sizes also suggested positive associations (0.65 mmHg and 0.60 mmHg per 10 µg/m³ PM_2.5, respectively), but these did not reach statistical significance and were accompanied by substantial heterogeneity (I² = 87.0% and 96.3%) (Fig. 3A).

Fig. 3.

Subgroup meta-analysis of SBP (A) and DBP (B) per 10 µg/m³ increase in PM_2.5 exposure, stratified by sample size

For DBP, very large subgroup (2 studies) showed a pooled increase of 0.25 mmHg per 10 µg/m³ PM_2.5 (95% CI: − 0.01 to 0.51), but heterogeneity remained maximal (I² = 99.9%). Small (5 studies) and medium (2 studies) cohorts also suggested positive associations (0.38 mmHg and 0.57 mmHg per 10 µg/m³ PM_2.5, respectively), though these were not statistically significant and were accompanied by substantial heterogeneity (I² = 89.6% and 85.0%). (Fig. 3B)

Exposure duration

In the overall analysis, SBP increased significantly with PM_2.5 exposure, whereas DBP showed no significant association. Subgroup analysis by exposure duration revealed positive but heterogeneous effects for both outcomes. For SBP, short-term exposure (6 studies) produced a pooled estimate of 0.56 mmHg per 10 µg/m³ PM_2.5 (95% CI: − 0.36 to 1.47; I² = 90.3%), and long-term exposure (4 studies) yielded 1.94 mmHg (95% CI: − 0.18 to 4.06; I² = 99.8%), neither reaching statistical significance (Fig. 4A). For DBP, short-term exposure (7 studies) indicated a non-significant increase of 0.43 mmHg (95% CI: − 0.48 to 1.34; I² = 86.8%), whereas long-term exposure (3 studies) showed a statistically significant increase of 0.33 mmHg (95% CI: 0.06 to 0.60; I² = 99.9%) (Fig. 4B).

Fig. 4.

Subgroup meta-analysis of SBP (A) and DBP (B) per 10 µg/m³ increase in PM_2.5 exposure, stratified by exposure duration

Population type subgroup analysis

Subgroup analysis stratified by population type showed that university students exhibited larger pooled effect estimates than the general population. For university students, the pooled increase was 1.47 mmHg per 10 µg/m³ PM_2.5 for SBP (6 studies, 95% CI: −0.21 to 3.14; I² = 97%) (Fig. 5A) and 0.53 mmHg per 10 µg/m³ PM_2.5 for DBP (7 studies, 95% CI: −0.39 to 1.44; I² = 88.3%) (Fig. 5B). In contrast, for the general population, the pooled estimates were smaller: 0.31 mmHg per 10 µg/m³ PM_2.5 for SBP (4 studies, 95% CI: −0.03 to 0.65; I² = 99.8%) (Fig. 5A) and 0.19 mmHg per 10 µg/m³ PM_2.5 for DBP (3 studies, 95% CI: −0.02 to 0.39; I² = 99.9%) (Fig. 5B). Of these, only the pooled effect for SBP among university students reached statistical significance, while all other associations were not statistically significant.

Fig. 5.

Subgroup meta-analysis of SBP (A) and DBP (B) per 10 µg/m³ increase in PM_2.5 exposure, stratified by population type

PM_2.5measurement method subgroup analysis

For SBP, personal monitoring (2 studies) showed a non-significant pooled effect of −0.21 mmHg per 10 µg/m³ PM 2.5 (95% CI: −0.71 to 0.29; I² = 70.1%), whereas ambient monitoring (8 studies) demonstrated a pooled increase of 1.52 mmHg (95% CI: 0.29 to 2.74), with extremely high heterogeneity (I² = 99.7%) (Fig. 6A). For DBP, personal monitoring (2 studies) indicated a non-significant effect of −0.03 mmHg (95% CI: −0.26 to 0.20; I² = 38.6%), while ambient monitoring (8 studies) showed a pooled increase of 0.53 mmHg (95% CI: −0.20 to 1.27), also with very high heterogeneity (I² = 99.7%) (Fig. 6B). Across both blood pressure outcomes, ambient exposure studies consistently reported positive associations with blood pressure, while personal exposure studies showed small, non-significant decreases.

Fig. 6.

Subgroup meta-analysis of SBP (A) and DBP (B) per 10 µg/m³ increase in PM_2.5 exposure, stratified by PM_2.5 exposure assessment method

Study design subgroup analysis

Subgroup analysis according to study design showed that cross sectional studies produced stronger associations for both SBP and DBP compared to longitudinal studies. For SBP, the pooled effect estimates from longitudinal studies (7 studies) per 10 µg/m³ increase in PM_2.5 was 0.53 mmHg (95% CI: −0.32 to 1.38; I² = 70.1%) while cross-sectional studies (3 studies) indicated substantial increase of 2.14 mmHg (95% CI: −0.33 to 4.61) with extremely high heterogeneity (I² = 99.7%) (Fig. 7A). For DBP, longitudinal studies reported a pooled increase of 0.26 mmHg (95%CI: −0.57 to 1.09; I² = 88.8%), whereas cross-sectional studies exhibited a statistically significant association of 0.61 mmHg (95% CI: 0.19 to 1.03; I² = 60.5%) (Fig. 7B).

Fig. 7.

Subgroup meta-analysis of SBP (A) and DBP (B) per 10 µg/m³ increase in PM_2.5 exposure, stratified by study design

Publication bias (funnel plot)

To evaluate the presence of publication bias, funnel plot analysis and statistical tests (Begg’s and Egger’s) were performed. Funnel plot analysis suggested some asymmetry for both SBP (10 studies) and DBP (10 studies), with smaller studies tending to report larger positive effect sizes (Fig. 8A and B). However, Begg’s test did not indicate statistically significant publication bias for either SBP (p = 0.180) or DBP (p = 0.128). Similarly, Egger’s regression test was not significant for SBP (p = 0.326) or DBP (p = 0.332). Given the small number of included studies, these statistical tests may have limited power, and the possibility of publication bias cannot be excluded.

Fig. 8.

Funnel plots assessing publication bias for SBP (A) and DBP (B)

Sensitive analysis (leave one out)

Leave-one-out analyses were performed to assess the influence of individual studies on the pooled estimates. For SBP, effect sizes ranged from 0.54 to 1.33 mmHg per 10 µg/m³ PM_2.5, with the direction of association consistently positive; omission of some studies slightly reduced precision, but the overall findings remained robust. For DBP, pooled estimates ranged from 0.19 to 0.60 mmHg, with several iterations crossing the null, indicating reduced stability compared with SBP. In both cases, no single study substantially altered the overall inference, suggesting that the meta-analysis results were not driven by any individual study (Supplementary Figs. 1, 2).

Discussion

Summary of key findings

This systematic review and meta-analysis examined the association between PM_2.5 exposure and blood pressure among young adults in Asia. Overall, most included studies revealed positive association, with SBP exhibiting more consistent increases. Long-term PM_2.5 exposure was significantly associated with elevated DBP, suggesting cumulative vascular effect over time. Although meta-regression could not be performed due to the limited number of eligible studies, subgroup analyses consistently indicated positive associations across population type, exposure duration, exposure measurement, study design, and sample size. Substantial heterogeneity was observed across studies, highlighting the need for cautious interpretation.

Sub-group analysis key findings

PM 2.5 exposure measurement (personal vs ambient)

According to our subgroup analysis, studies utilizing personal monitoring reported null or weaker associations with blood pressure compared to those relying on ambient monitoring. Although personal monitoring can be affected by microenvironmental influences (e.g., ventilation, cooking, smoking, temperature) and methodological challenges, including device calibration, inconsistent wearing times, and shorter monitoring durations [37–41], these issues should be regarded as methodological considerations rather than inherent limitations of the approach. Importantly, personal monitoring provides exposure estimates that are, in principle, closer to true inhaled PM_2.5 dose than outdoor ambient proxies as that they more directly capture individual microenvironments and activity patterns.

Another key consideration is that in rapidly urbanizing Asian settings, urbanization driven pollution and human activities including traffic emissions, industrial processes, and land use patterns not only elevate PM_2.5 concentrations but also diversify its chemical composition, thereby modifying spatial variability [42, 43]. Moreover, atmospheric dispersion and diurnal variation may complicate exposure assessment, with some evidence suggesting elevated nighttime concentrations in urban environments [44, 45]. In addition, outcome assessment heterogeneity adds another layer of complexity. For example, Baccarelli et al. [28] employed non-ambulatory blood pressure measurements, whereas Ren et al. [30] utilized ambulatory monitoring, which better captures diurnal variation [28, 30].

Only two studies employing personal monitoring were included, providing insufficient evidence to conclude that personal PM₂.₅ exposure is not positively associated with BP. Specifically, Ren et al. [30] examined exposure–outcome associations at an hourly time scale, whereas most other studies focused on daily or longer‑term exposures, and also reported that BP responses to very short‑term PM_2.5 exposures may differ mechanistically from those associated with longer‑term windows. Accordingly, the inverse association reported in their study is more likely explained by differences in exposure duration and physiological response than by methodological distinctions between personal and ambient monitoring.

Population type subgroup (university students vs general population)

We stratified population type into two groups, as more than half of the included studies focused on university students while the reminder included young adults from general population. University students exhibited larger pooled increases in SBP and DBP compared to the general young adult population, although none of these associations reached statistical significance. This distinction is important because differences in PM_2.5 exposure sources and population characteristics may contribute to the heterogeneity in outcomes. University students spend extended periods in unique campus environments characterized by concentrated traffic emissions and localized industrial activity. In contrast, the general population includes young adults residing in more diverse geographic settings, ranging from highly urbanized canters to peri-urban or rural areas, where household air pollution (e.g., cooking, smoking), local ventilation, weather conditions and seasonal outdoor sources such as forest burning may play a greater role. Evidence from Luo et al. [46] confirms that PM_2.5 toxicity varies by source, with particles derived from automobile exhaust, coal combustion, and biomass burning differing substantially in chemical composition and cytotoxicity. Traffic‑related PM_2.5 exhibited the strongest overall toxicity, attributable to enrichment in carbonaceous fractions and transition metals, whereas coal combustion PM_2.5 elicited more pronounced inflammatory responses due to elevated lead (Pb), copper (Cu), and arsenic (As) content [46]. These findings suggest that source‑specific PM_2.5 profiles may plausibly contribute to heterogeneity in BP outcome across subgroups.

In addition to differences in PM_2.5 source profiles, population characteristics themselves may also play an important role. University student populations typically exhibit more uniform activity patterns, living environments, and lifestyles, which may reduce unmeasured confounding and strengthen observed associations. In contrast, general populations display broader variability in occupational exposures, housing conditions, and behavioural factors, which may attenuate associations. Overall, both differences in PM_2.5 source profiles and population‑level heterogeneity likely contribute to the observed subgroup differences.

Exposure duration subgroup (short term vs long term)

Our analysis indicates that long-term exposure to PM_2.5 is associated with stronger and more consistent elevations in blood pressure compared to short-term exposure. This pattern likely to reflect that short-term exposure is associated with acute autonomic imbalance, whereas long term exposure amplifies cumulative vascular injury through sustained inflammation and endothelial dysfunction. Methodological differences may also contribute, such as the utilization of hourly or lag-day models that capture acute effects but overlook cumulative impacts, whereas long-term investigations rely on annual averages, requiring harmonized protocols and rigorous adjustment for confounders.

Additionally, although several potential sources were investigated, extremely high heterogeneity persisted across studies. This variability likely reflects limited adjustment for important confounders such as sex, lifestyle behaviours, socioeconomic status, family history of hypertension, and co‑exposures to other pollutants, which may have introduced residual bias and further amplified heterogeneity. Nevertheless, despite the observed variability, overall and most sub-group analysis consistently demonstrated positive association between PM_2.5 exposure and both SBP and DBP among young adults living in highly polluted Asian region. While the heterogeneity reduces the precision of pooled estimates, acknowledging these sources of variability enhances transparency and underscores the need for future research employing standardized exposure and outcome protocols with comprehensive confounder control.

Comparison with previous reviews

Our findings are consistent with prior systematic reviews and meta-analyses linking PM_2.5 exposure with blood pressure. Liang et al. [8], synthesizing 22 studies, found positive associations for both SBP and DBP, including in subgroup analyses restricted to Asian populations, which supports the robustness of our findings despite differences in study populations [8]. Yang et al. (2018) also observed significant increases in blood pressure associated with long-term PM₂.₅ exposure, although the association with SBP reached statistical significance only [7]. More recently, Niu et al. (2021), in a comprehensive meta-analysis of long term PM₂.₅ exposure and blood pressure and hypertension, reported modest but significant increases in SBP and DBP [9], based largely on cohorts from high-income countries and general populations. Our review adds to this evidence base by focusing specifically on young adults in Asia, a population underrepresented in earlier syntheses. Importantly, we included six recent studies not covered in previous reviews [27, 29, 31, 34–36], thereby expanding the available evidence. In addition, our analysis examined both short- and long-term exposures, providing greater granularity in characterizing exposure–response relationships in this age group.

Biological mechanisms

There are four mains mechanisms by which inhaled particulate matter affects the cardiovascular system have been proposed: (a) Inhaled particulate matter reaches the terminal bronchioles and enters the alveoli, inducing an inflammatory response in the lung, (b) Released inflammatory mediators and unidentified mediators enter the circulation, (c) A small proportion of particles reach the circulation and (d) Inhaled particulate matter activates alveoli sensory receptors, leading to autonomic imbalance [4]. These primary mechanisms may promote physiological pathways such as endothelial dysfunction, thrombotic pathways, activation of hypothalamo-piturary axis and epigenomic changes. Over time, the cumulative impact of these pathways increasing the risk of cardiovascular diseases including hypertension, atherosclerosis, myocardial dysfunction and stroke [47]. Evidence from panel study in young adults shows that traffic-related PM_2.5 exposure reduces heart rate variability, indicating autonomic dysregulation [48] while experimental study reports bidirectional disruption of autonomic balance [49]. Long-term exposure has been linked to impaired endothelial function and reduced flow-mediated dilation [50], while short-term concentrated PM_2.5 exposure like inhalational diesel exhaust can cause reversible endothelial dysfunction within hours [51, 52]. These mechanisms clarify why long-term exposure produce stronger and more consistent blood pressure elevations than short-term exposure as acute autonomic responses drive transient increases, whereas chronic endothelial injury and vascular remodelling sustain persistent elevations. Overall, these findings underscore the need for integrated study designs that capture both acute and cumulative effects to inform prevention strategies in polluted regions.

Public health relevance

Hypertension in young adults is rising globally, however, systematic reviews examining the association between PM_2.5 exposure and blood pressure in young adults living in Asia where PM_2.5 concentrations are disproportionately high remain limited. Recent evidence indicates that 99% of the population across East Asia are exposed to average annual PM_2.5 concentrations of approximately 45.2 µg/m³, a level that far exceeds the World Health Organization (WHO) Air Quality Guideline (AQG) of 5 µg/m³ for annual mean exposure [53]. In addition, rapid urbanization may further magnify PM_2.5‑related health effects by intensifying personal exposure through traffic congestion, limited green space, and lifestyle changes that increase cardiovascular morbidity [54]. Studies from Asia also demonstrate significant associations between PM_2.5 exposure and elevated blood pressure, particularly in urban cohorts [55].

Findings from our analysis consistently revealed associations between PM_2.5 and blood pressure, even in normotensive cohorts. Data from 4.5 million young adults show that high‑normal blood pressure (130–139/85–89 mmHg) already confers elevated cardiovascular risk with Asian studies reporting relative risks rising from 1.18 in normotensive individuals to 2.86 in grade 2 hypertension [56]. This pattern highlights the synergistic potential of PM_2.5 exposure and early-onset hypertension in accelerating adverse cardiovascular outcomes, emphasizing the urgent need for preventive strategies targeting young adults in polluted regions where health care system already faces constraints in addressing early cardiovascular risk.

Moreover, according to WHO PEN (Package of Essential Noncommunicable Disease) interventions which has been widely implemented in primary care settings across low- and middle-income countries, cardiovascular risk screening typically begins at age 40 and above [57]. However, young adults residing in highly polluted regions are often subjected to prolonged and unmitigated exposure to ambient air pollutants, particularly PM_2.5. In the absence of early screening and timely clinical intervention, the chronic exposure to PM_2.5, may contribute to increased vulnerability to premature cardiovascular morbidity. Collectively, the current findings underscore the critical need to address PM_2.5 related high blood pressure and advocate for earlier cardiovascular risks screening in young adults particularly in the regions with high pollution burdens.

Strengths and limitations

This review targets a previously understudies population and represents the first meta-analysis to evaluate the association of PM_2.5 exposure and BP in young adults residing in Asia, where PM_2.5 levels are among the highest globally due to rapid urbanization, industrialization, biomass burning, and vehicle emissions. It includes six new studies not covered by previous meta-analysis, thereby enhancing the comprehensiveness of the available evidence. Additionally, it provides detailed subgroup analyses by exposure type (ambient and personal), exposure duration, study design, sample size and population, providing comprehensive insights into the associations between PM_2.5 exposure and blood pressure.

The limitations for the study should be noted as follows: firstly, high heterogeneity existed across studies, potentially attributable to differences in sample size, study designs, exposure assessment methods, blood pressure measurement techniques, geographic settings, population characteristics, cofounding control and covariate adjustments. Secondly, the included studies were geographically restricted to mainland China, Taiwan and Hongkong, limiting generalizability of findings to other Asian countries including South, Southeast and central Asia. This geographic restriction observed in our study may partly reflect variability in age definitions of young adults as well as inconsistencies in blood pressure data reporting for age groups across studies conducted in other Asian countries. Moreover, our emphasis on ambient pollution exposure rather than indoor air pollution likely contributed to omission of other relevant research from these regions. Although household pollution studies were excluded to maintain consistency in exposure metrics, we acknowledge that personal monitoring inevitably captures both indoor and outdoor contributions. This overlap enhances ecological validity by reflecting the real-world exposure profile of young adults in polluted regions, while ambient PM_2.5 remains the dominant public health concern. Thirdly, the modification effects of other pollutants were not assessed in this study due to inconsistent control for other pollutants and also variability in PM_2.5 compositions in different studies. Finally, sex specific and socioeconomic stratification was limited in included studies, constraining the ability to explore differences in susceptibility across demographic subgroups.

Future directions

Future longitudinal studies with broader geographic coverage and harmonized age groups, exposure duration, exposure assessment and measurement methods are essential to inform age specific policy intervention and enhance regional representativeness. Additionally, studies should incorporate assessments of gender and socioeconomic status in association between air pollution and BP among young adults while adjusting for other possible cofounding factors such as dietary factors, stress, physical activity and environmental factors to identify overlooked subpopulations and guide targeted interventions. Moreover, studies exploring mediating biological pathways through the investigation of relevant biomarkers are warranted to advance both epidemiological and clinical applications.

Conclusion

In conclusion, the findings from our study demonstrated that exposure to PM_2.5 was associated with increased SBP and DBP among young adults residing in East Asian contexts. Given the rising global burden of air pollution and early-onset hypertension, our findings underscore a critical need for age specific policy interventions as well as highlight the importance of targeted screening and prevention strategies among young adults in highly polluted regions. To establish broader generalizability, future studies should extend to other Asian regions and focus to elucidate casual relationships and underlying biological mechanisms concerning PM_2.5 exposure on blood pressure and cardiovascular risks.

Abbreviations

BP

Blood pressure

SBP

Systolic blood pressure

DBP

Diastolic blood pressure

PM_2.5

Particulate matter ≤ 2.5 μm in diameter

RR

Relative risk

Authors’ contributions

SWM and HC contributed to the conceptualization of the study. SWM, BZ and HC conducted data curation and formal analysis. SWM, BZ, AI, and HC were responsible for the investigation and methodology. SWM and HC drafted the original manuscript, while SH, WP and KK provided validation and supervision. All the authors read and approved the final manuscript.

Funding

There is no specific fund for conducting the research.

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.