This study is a systematic review and meta-analysis that synthesizes observational studies on the association between long-term ambient particulate matter exposure and AD incidence/diagnosis. It strictly adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020 Statement). The study protocol was prospectively registered on PROSPERO (International Prospective Register of Systematic Reviews), with the registration number [CRD420251174986].

This study adopted a comprehensive search strategy using English databases to cover published studies worldwide on the association between long-term ambient particulate matter exposure and Alzheimer’s disease. Specifically, four core English databases were searched: PubMed, Embase, Cochrane Library, and Web of Science Core Collection, to ensure the comprehensiveness and accuracy of the search.

Search terms were constructed based on the dual themes of “exposure factor—outcome indicator” and adjusted to MeSH terms (e.g., for PubMed) or free-text terms according to the characteristics of each database, to maximize search comprehensiveness. Core search terms were categorized as follows: Particulate matter-related: “Particulate Matter,” “PM2.5,” “Ultrafine Particles,” “PM10,” “Particulate Air Pollutants”, “Airborne Particulate Matter”, “Ambient Particulate Matter”, “Ultrafine Fibers”; AD-related: “Alzheimer’s disease,” “AD,” “Familial Alzheimer Diseases”, “Focal Onset Alzheimer’s Diseases”, “Acute Confusional Senile Dementia”, “Senile Dementia.” Retrieval time frame: From the establishment of each database to September 12, 2025, to ensure the inclusion of the latest research. Language restriction: Only studies published in English. Key information (e.g., exposure assessment, outcome data, effect sizes) was co-translated by two researchers with sufficient language skills. This avoided language bias. Based on the PICO principle, the inclusion and exclusion criteria were clearly defined to ensure consistency in the definition of study participants, exposures, and outcomes. Details are presented in Table 1.

Literature screening was independently done by two trained researchers (Na Zhao and Hong Sun), who have backgrounds in environmental epidemiology and neuroscience. Screening included two steps: ① Initial screening: Excluded studies that clearly failed inclusion criteria (e.g., animal experiments, cross-sectional studies) based on titles and abstracts. Potentially eligible studies were flagged for full-text review. ② Full-text screening: Obtained the flagged texts and assessed each against inclusion criteria individually.

Disagreements between the two on eligibility were resolved through discussion, or by third-party arbitration from a senior epidemiologist with relevant expertise if consensus failed to be reached, and a final list of included studies was finalized.

A self-designed Data Extraction Form was used, with extraction independently performed by the two and cross-checked for accuracy. Extracted information included: basic study details [first author, publication year, country/region, study type (prospective/retrospective cohort), sample size (total cohort)]; population characteristics [age (mean ± SD/median), gender ratio, AD diagnosis method, particulate type (PM_2.5/PM₁₀, etc.), exposure assessment method, average follow-up (years), exposure window, average exposure level]; and original effect sizes [hazard ratio (HR), relative risk (RR), odds ratio (OR)] and corresponding 95% CI.

Study quality was assessed using the Newcastle–Ottawa Scale (NOS) for observational studies—an internationally recognized tool to assess bias risk in cohort/case–control studies for systematic reviews, with eight items (total score: 9 points) that categorize studies as high quality (≥7 points), moderate quality (4–6 points), or low quality (≤3 points, excluded); its dimensions (for cohort studies) cover participant selection (4 points, 1 for defining exposure groups, 1 for representativeness of unexposed/exposed groups, 1 for no baseline AD, 1 for adequate sample size), comparability (2 points, 1 for adjusting key confounders, 1 for adjusting other potential ones like education and comorbidities), and outcome assessment (3 points, 1 for reliable outcome measurement such as clinical diagnosis + imaging confirmation, 1 for adequate follow-up ≥5 years, 1 for attrition <20% or proper handling). Two researchers (Na Zhao and Hong Sun) conducted the assessment independently, with disagreements resolved via discussion or third-party arbitration, and a final quality assessment table for included studies was generated.

Meta-analysis was performed using R software (version 4.4.2; R Foundation for Statistical Computing, Vienna, Austria) with the metafor package with two-tailed tests (α = 0.05). Heterogeneity was assessed via Q-test (p < 0.10 = significant heterogeneity) and I² statistic: I² < 25% (low): fixed-effects model; 25% ≤ I² ≤ 50% (moderate): fixed-effects if study design/population consistent, else random-effects; I² > 50% (high): random-effects (DerSimonian-Laird method), with subgroup analysis/meta-regression to explore sources. Random-effects models were preferred for synthesizing heterogeneous evidence as they account for both within-study sampling error and between-study variability, which is more robust for environmental epidemiology studies with inherent methodological differences.

For cohort studies, HR (95% CI) was used as effect size; multivariate-adjusted effect size was prioritized over unadjusted. For continuous exposures (e.g., PM_2.5) with inconsistent dose increments (5/10 μg/m³), all effect sizes were first standardized to HR per fixed dose (e.g., 5 μg/m³ PM_2.5) before random-effects pooling.

Heterogeneity sources were analyzed via meta-regression, subgroup, and sensitivity analyses. Meta-regression included exposure assessment method, follow-up duration, study region, and sample size as moderators to explore potential influencing factors. Subgroup analysis stratified by study region and methodology (with exposure assessment method as the core stratification factor), locating heterogeneity sources by re-pooling effect sizes and comparing heterogeneity indices. Sensitivity analysis excluded one study at a time to observe fluctuations in pooled HR (95% CI) and compared fixed- vs. random-effects results to verify stability.

Reliability of the results was verified by sensitivity analysis and publication bias assessment: Sensitivity analysis adopted the one-study removal approach, sequentially excluding each included study and re-pooling effect sizes to examine changes in hazard ratios (HRs) for result stability; publication bias was evaluated visually via funnel plots (x-axis = lnHR, y-axis = SE(lnHR), symmetry indicating no significant bias) and quantitatively using Egger’s test (p < 0.05 = significant bias), with the trim-and-fill method applied to adjust effect sizes if bias was detected.

This study retrieved a total of 1,428 relevant articles from databases: PubMed (n = 436), Embase (n = 518), Web of Science (n = 473), and Cochrane Library (n = 1). After removing duplicates using EndNote X9 software, 990 articles remained and proceeded to the initial screening stage.

During initial screening, 588 articles that did not meet the inclusion criteria were excluded based on titles and abstracts, with specific reasons including: animal experiments (n = 138), review articles (n = 158), cross-sectional studies (n = 76), outcomes limited to cognitive decline (n = 136), and short-term particulate matter exposure (<1 year, n = 80). A total of 402 articles were retained for full-text screening, during which 377 additional articles were excluded. The main exclusion reasons were: no clear AD diagnostic criteria (n = 149), failure to extract effect sizes and 95% confidence intervals (95% CIs, n = 52), study populations being occupationally exposed (n = 145), and conference abstracts with incomplete data (n = 31).

Finally, 25 studies that met the criteria were included. See Figure 1 for details of the literature screening process.

The 25 included studies (8, 10–33) were published between 2015 and 2025, including 15 prospective cohort studies (60.0%) and 10 retrospective cohort studies (40.0%). Geographically, 5 studies were from Asia (4 from China and 1 from South Korea), 19 from Europe [including 7 from the United Kingdom (UK)], and 1 from Oceania. Sample sizes ranged from 572 to 50,053,399 participants, with a total study population exceeding 171,896,112. Most participants were aged 50–85 years; some studies focused on single-gender populations (only males or only females), and gender ratios were reported in most cases. AD was defined via two approaches: diagnosis based on administrative databases and self-report. Regarding pollutant types, 22 studies analyzed PM_2.5, 8 analyzed PM₁₀, 12 analyzed NO₂, 5 analyzed NO_x, and only 4 analyzed O₃. See Table 2 details.

Details on exposure assessment and outcomes of the 25 included studies (8, 10–33) are shown in Table 3. The main exposure assessment method was land use regression (LUR) models (11 studies), followed by machine learning ensemble models (5 studies), direct monitoring (3 studies), atmospheric chemical transport models (1 study), and Bayesian maximum entropy spatiotemporal models (1 study). The average follow-up duration was 2–22.7 years, with most studies (22 studies) having a follow-up of 6–12 years. Exposure assessment windows included single-year, 5-year moving average, annual average during follow-up, 1-year average before baseline, and cumulative exposure. Effect sizes in all studies were presented as hazard ratios (HRs) and standardized for exposure increments in accordance with WHO guidelines.

Bias risk assessment of the 25 included studies (8, 10–33) was performed using the NOS for cohort studies, covering three core dimensions: participant selection, inter-group comparability, and outcome assessment. The re-evaluated scores showed a reasonable distribution across high-quality tiers: 14 studies achieved a full score of 9/9 (fully meeting all NOS criteria), 8 studies scored 8/9 (with minor deductions for limited adjustment of secondary confounders or unclear follow-up attrition rates), and 3 studies scored 7/9 (with deductions for specific population selection and partial confounding adjustment). All included studies attained a NOS score ≥7/9, indicating high-quality evidence that guarantees the reliability of the meta-analysis results (Table 4).

We used a random-effects model because heterogeneity across studies was extremely high (I² = 97.8%, p < 0.001). This heterogeneity violates the assumption of a common true effect needed for fixed-effects models. The random-effects model accounts for both within-study sampling error and between-study variability, making results more reliable and generalizable. For meta-analysis of 22 studies (10–13, 15–23, 25–33) to assess the association between a 5 μg/m³ increase in PM_2.5 exposure and incident AD risk, results showed a significant positive association: pooled HR = 1.24 (95% CI = 1.10–1.39, p < 0.001).

We used meta-regression to explore heterogeneity, with moderators including exposure assessment method, follow-up duration, study region, and sample size. The overall model was significant (QM = 101.26, p < 0.0001), explaining 86.05% of heterogeneity (R² = 86.05%). Though no single moderator reached significance, direct ground monitoring (DirectMon, p = 0.0986) and Bayesian maximum entropy spatiotemporal model (p = 0.0868) showed marginal associations (positive estimates); other factors (follow-up, region, sample size) had no significant contributions. Residual I² remained high (97.36%), but tau² decreased from 0.0693 to 0.0097, confirming moderators as major heterogeneity sources.

Subgroup analysis (by simplified exposure methods: LUR, MLEns, DirectMon, Others) further verified significant between-subgroup differences (Q = 24.66, p < 0.0001): DirectMon (n = 2) had the strongest association (HR = 2.35, 95% CI: 1.74–3.18), followed by LUR (k = 10, HR = 1.15) and MLEns (n = 5, HR = 1.10); the “Others” subgroup (n = 5) showed no significant association (HR = 1.17, 95% CI: 0.996–1.38) due to rare methods with small samples. These subgroup trends aligned with meta-regression, confirming exposure assessment method as the core heterogeneity source (Figure 2).

A random-effects model was adopted given the high between-study heterogeneity (I² = 93.4%, p < 0.0001), which accounts for both sampling error and inherent variability across studies. This model was used to analyze the association between a 10 μg/m³ increase in PM10 exposure and incident AD risk, with subgroup stratification by study design (retrospective vs. prospective). Retrospective subgroup (4 studies) (10, 24, 26, 28): no significant association (HR = 1.02, 95% CI = 0.90–1.17) with moderate heterogeneity (I² = 55.4%, p = 0.081). Prospective subgroup (4 studies) (8, 10, 23, 25): significant positive association (pooled HR = 1.27, 95% CI = 1.19–1.35) with very low heterogeneity (I² = 0%, p = 0.676). Overall (8 studies): significant association (HR = 1.16, 95% CI = 1.01–1.33) with high heterogeneity (I² = 93.4%, p < 0.0001). Subgroup heterogeneity test (p = 0.004) identified study design as an important heterogeneity source, with a more pronounced PM10-AD association in prospective studies (Figure 3).

A random-effects model analyzed the association between a 10 μg/m³ NO₂ increase and incident AD risk, with subgroup stratification by region. UK subgroup (6 studies) (10, 13, 18, 23, 27, 29): significant association (HR = 1.13, 95% CI = 1.09–1.16), low heterogeneity (I² = 13.3%, p = 0.33). Others subgroup (4 studies) (15, 20, 26, 28): no significant association (HR = 0.96, 95% CI = 0.90–1.02), moderate heterogeneity (I² = 26.2%, p = 0.25). America subgroup (2 studies) (17, 30): slight positive association (HR = 1.03, 95% CI = 1.02–1.04), low heterogeneity (I² = 14.5%, p = 0.280). Overall (12 studies): significant association (HR = 1.06, 95% CI = 1.00–1.12), high heterogeneity (I² = 82.9%, p < 0.001). Subgroup difference test (p < 0.0001) identified region as an important heterogeneity source, with a more significant NO2-AD association in the UK (Figure 4).

A random-effects model analyzed the association between a 10 μg/m³ NOₓ increase and incident AD risk, with subgroup stratification by average follow-up duration. Follow-up >12 years (2 studies) (14, 25): significant association (HR = 1.29, 95% CI = 1.11–1.48), no heterogeneity (I² = 0%, p = 0.553). Follow-up <12 years (3 studies) (10, 23, 29): significant association (HR = 1.05, 95% CI = 1.03–1.07), no heterogeneity (I² = 0%, p = 0.924). Overall (5 studies): significant association (HR = 1.05, 95% CI = 1.03–1.07), moderate heterogeneity (I² = 51.5%, p = 0.083). Subgroup difference test (p = 0.005) identified follow-up duration as an important heterogeneity source, with a stronger NOₓ-AD association in the long follow-up subgroup (>12 years) (Figure 5).

A random-effects model analyzed 4 studies (11, 13, 17, 30) to assess the association between a 60 μg/m³ O3 increase and incident AD risk. Results showed no statistically significant association (HR = 1.23, 95% CI = 0.65–2.31, p > 0.05), with extremely high heterogeneity (I² = 99.8%, p < 0.001). Notably, the included studies exhibited opposing effect directions: one study reported a strong positive association (HR = 3.12), while another showed a non-significant negative association (HR = 0.78). Given the small number of included studies (n = 4), conflicting effect directions, and extreme heterogeneity, the current evidence is inadequate and unstable to confirm or refute an association between long-term O₃ exposure and incident AD risk. The pooled result should be interpreted with extreme caution (Figure 6).

Publication bias assessment results: For PM_2.5 and NO₂ (≥10 included studies), funnel plots showed symmetric distributions, suggesting low publication bias (Figure 7). For PM₁₀, NOₓ, and O₃ (<10 included studies), Egger’s test was used: PM₁₀ (p = 0.159) and O₃ (p = 0.395) had no significant publication bias; NOₓ (p = 0.047) showed potential bias, possibly due to underreporting of negative-result studies. The trim-and-fill method was applied to adjust for NOₓ’s potential publication bias. After imputing 1 missing negative-result study, the adjusted pooled HR for NOₓ was 1.02 (95% CI: 1.01–1.03, p < 0.001), which remained statistically significant.