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. 2026 Sep 19;5(4):100283. doi: 10.1016/j.eehl.2026.100283

Black carbon as a major contributor to ambient PM2.5-related cause-specific outpatient risks: Implications for early warning and health benefits evaluation of air quality improvement

Yang Lan a,b,c,d,1, Jian Lei a,b,c,d,1, Huimeng Liu a,b,c,d, Yunxing Jiang a,b,c,d, Chenlu Yang a,b,c,d, Qinchi Yu a,b,c,d, Jinxi Wang e, Chen Wei e, Yinxiang Li f, Jing Wei g, Xi Chen h, Furong Deng i, Xinbiao Guo i, Yuewei Liu j,⁎, Shaowei Wu a,b,c,d,k,⁎⁎
PMCID: PMC13639161  PMID: 42840506

Abstract

Ambient fine particulate matter (PM2.5) poses acute multi-organ health risks, but evidence on its constituent-specific early-stage morbidity risks remains limited. We conducted a nationwide time-stratified case-crossover study using over 212 million daily outpatient records for 12 major diseases from 274 Chinese cities during 2013–2017. City-specific associations between PM2.5 and its major constituents and cause-specific outpatient risks were estimated using conditional logistic regression, and pooled by random-effects models. Robustness and independence of constituent-specific associations were tested using three complementary models. Attributable fractions of outpatient visit burdens were quantified. Black carbon (BC) showed the strongest independent associations at lag0 with most cause-specific outpatient visits, in contrast to lag0-1 with most cause-specific hospital admissions. Estimated increases (95% confidence intervals) in outpatient visits ranged from 1.33% (0.93–1.74) for chronic kidney disease (CKD) to 3.89% (3.30–4.49) for chronic obstructive pulmonary disease (COPD) per interquartile range increase (1.8 μg/m3) in BC at lag0, with corresponding attributable fractions ranging from 2.38% (CKD) to 6.12% (COPD). Following stringent clean air policies since 2013, annual attributable fractions of PM2.5- and BC-associated outpatient visit burdens declined substantially, with relative reductions larger for outpatient visits than for hospital admissions for both PM2.5 (40.61% vs 30.00%) and BC (28.66% vs 21.14%). In conclusion, BC is a major contributor to PM2.5-associated cause-specific outpatient risks. Prioritizing the control of key PM2.5 constituents (e.g., BC)—rather than total PM2.5 mass alone—may help mitigate acute multi-organ morbidity risks. Outpatient visits may serve as a sensitive early-warning indicator for air pollution-related morbidity risks.

Keywords: Air pollution, Black carbon, Early-stage morbidity risk, Multi-organ system, Outpatient visit

Graphical abstract

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Highlights

  • •

    Black carbon (BC) drives PM2.5-related outpatient risks in multi-organ systems.

  • •

    Distinct time-window sensitivities in outpatient visits vs. hospital admissions.

  • •

    More reductions in PM2.5- & BC-attributable outpatient visits than hospital admissions.

  • •

    Outpatient visit is a sensitive early-warning indicator for air pollution health risks.

  • •

    Constituent-targeted (e.g., BC) air pollution control may be a cost-effective strategy.

1. Introduction

Ambient particulate matter pollution is a leading global environmental health hazard, contributing 8.2% of global disability-adjusted life years in 2023 [1]. Within the particulate matter complex, ambient fine particulate matter (PM2.5) has emerged as the most health-relevant air pollutant. Exposure to ambient PM2.5 has been linked to acute and chronic adverse outcomes across multi-organ systems, including respiratory [[2], [3], [4]], cardiovascular [[5], [6], [7]], mental-neurological [[8], [9], [10]], and kidney diseases [11,12]. As a complex mixture with heterogeneous toxic constituents, the health effects of PM2.5 may vary according to its chemical composition, among which carbonaceous components [including black carbon (BC) and organic matter (OM)] and inorganic ions (including sulfate, nitrate, ammonium, and chloride) are the major constituents [13,14]. These major constituents account for the vast majority of the PM2.5 mass, with other constituents (e.g., metals) present in relatively trace amounts. Emerging research has explored constituent-specific health effects of PM2.5 [[15], [16], [17]], but consensus on its most toxic constituents remains elusive, and most investigations have been restricted to limited regions or small sample sizes. While the World Health Organization (WHO) has recognized the importance of BC by issuing relevant good practice statements, it also emphasized that robust supporting evidence remains limited and urgently needed [18].

Notably, existing studies have predominantly focused on hospital admissions and mortality as outcomes [4,6,19], while outpatient visits—a sensitive indicator of early-stage disease symptoms—have been far less investigated. Unlike hospital admissions or mortality that reflect severe and advanced disease states, outpatient visits could capture mild and initial health impairments in individuals with symptoms that do not require hospitalization or involve life-threatening conditions [20]. Investigating outpatient risks associated with air pollution is critical for assessing the early health effects of air pollution, enabling proactive warning of potential severe disease progression and timely interventions to prevent subsequent hospital admissions and mortality. Additionally, evidence for the potential effects of PM2.5 constituents on mental-neurological and kidney disease remains scarce and inconsistent [16,21], despite these diseases ranking among the top global disease burden causes [22,23].

Building on our prior work that quantified the health benefits in cause-specific hospital admissions attributable to ambient air pollution reduction due to clean air policies in China [24], this study shifts focus to earlier-stage morbidity risks (prior to severe exacerbation requiring hospital admissions) to investigate whether PM2.5 and its major constituents exhibit consistent adverse impacts in early disease states. We conducted the largest nationwide study to date, using aggregated daily outpatient visit data from 274 Chinese cities at or above the prefecture level during 2013–2017 to examine the associations of short-term exposures to ambient PM2.5 and its major constituents with cause-specific outpatient visits across diseases affecting multiple organ systems and their major subtypes. This study aims to identify the primary toxic PM2.5 constituent(s) driving early-stage morbidity risks (i.e., outpatient visits) associated with PM2.5, to provide robust evidence to inform constituent-targeted air pollution control and disease prevention strategies. Given the growing challenges of further controlling total PM2.5 mass and increasing burdens of major diseases, targeting high-toxicity constituents may help safeguard public health by mitigating early-stage morbidity risks and preventing subsequent severe health outcomes.

2. Methods

2.1. Outpatient visit data collection

Daily outpatient visit data (2013–2017) were extracted from China’s two national basic medical insurance schemes (urban employee- and urban resident-based, respectively), which cover all tiers of medical institutions and had achieved 97.5% national coverage by 2014 [25], ensuring sample representativeness. Cause-specific outpatient visits were classified by ICD-10, encompassing respiratory [asthma, lower respiratory infections (LRI), and chronic obstructive pulmonary disease (COPD)], cardiovascular [cardiac arrhythmias, coronary heart disease (CHD), heart failure, and stroke], mental-neurological (depression, schizophrenia, Parkinson’s disease, and anxiety), and kidney diseases [chronic kidney disease (CKD)] (Table S1). To ensure statistical power, cities with <50 outpatient visits for any major disease during the study period were excluded, leaving 274 (81.1%) of 338 cities at or above the prefecture level for final analysis.

2.2. Environmental exposure assessment

Daily mean concentrations of PM2.5 and its major constituents (BC, sulfate, nitrate, ammonium, chloride, and OM) at a 1 km × 1 km spatial resolution were retrieved from the ChinaHighAirPollutants (CHAP) database; OM was estimated as total PM2.5 mass minus the sum of other major constituents. The CHAP-derived data showed good agreement with independent datasets. For PM2.5 concentrations, comparison with the China National Urban Air Quality Real-time Publishing Platform revealed strong concordance, with a Spearman correlation coefficient of 0.87 and an intraclass correlation coefficient of 0.84 [24]. Similarly, cross-validation with ground-based monitoring data for BC, sulfate, nitrate, ammonium, and chloride supported consistent performance, with R2 values of 0.66–0.82 and average root-mean-square errors ranging from 1.64 μg/m3 for BC to 6.61 μg/m3 for nitrate [26]. City-level concentrations were calculated as the arithmetic mean of all grid point values within the administrative boundaries.

Meteorological parameters [daily mean, maximum, and minimum ambient temperatures and relative humidity (RH)] were acquired from the China Meteorological Data Sharing Service System (http://data.cma.cn/), with city-level values averaged from valid monitoring stations. In addition, we acquired 2 m air temperature and 2 m dew-point temperature data from the land component of the fifth European Reanalysis (ERA5-Land) product, which has a 0.1° × 0.1° spatial resolution and hourly temporal resolution. ERA5-Land employs data assimilation techniques to generate temperature series highly consistent with surface weather station observations, building on the high spatiotemporal resolution of the parent ERA5 dataset [27]. Hourly temperature series for each study city were extracted from the nearest ERA5-Land grid cell, and daily mean temperatures were then derived by aggregating these hourly values. RH was calculated from air temperature and dew-point temperature via the Magnus formula [28].

2.3. Ethics approval

This study was exempted from institutional review board assessment by the Biomedical Ethics Committee of the Health Science Center at Xi'an Jiaotong University (No. 2021-1604).

2.4. Statistical analysis

We applied a three-stage analytical framework for this nationwide time-stratified case-crossover study. First, we constructed a case-crossover dataset for each cause-specific outpatient visit outcome in each city. The case days were defined as the dates of cause-specific outpatient visits. The remaining three or four same weekdays/weekends within the same one-month time stratum (i.e., matched by calendar year, month, day of the week, and city) were assigned as control days. A conditional logistic regression model was used to evaluate the city-specific associations between PM2.5 and its major constituents and cause-specific outpatient risks, with adjustment for a binary indicator for public holidays and natural cubic splines with six and three degrees of freedom for moving averages of daily mean temperature and RH of the present day and previous 21 days, respectively [24,29,30]. Second, a random-effects model was applied to pool these city-specific associations into overall associations, with the resulting effect estimates per 1 μg/m3 increase in PM2.5 and its major constituents subsequently transformed to an interquartile range (IQR) scale. Beyond the single-constituent model, we further used three complementary models—including remaining PM2.5 mass-adjusted, constituent residual, and two-constituent models—to test the robustness and independence of the associations between different major PM2.5 constituents and outpatient visits, as detailed in Extended Method S1. Drawing on prior evidence regarding PM2.5 constituent-related outpatient risks [31], we examined single-day (lag0, lag1, lag2) and cumulative (lag0-1, lag0-2) time windows (lag0 = the day of outpatient visit occurrence; lag0-1 = the day of outpatient visit occurrence plus the previous day). Associations between major PM2.5 constituents and cause-specific outpatient visits at time windows longer than lag2 or lag0-2 were generally statistically insignificant and thus were not shown in the present results. Finally, we quantified the attributable fractions of cause-specific outpatient visit burdens attributable to PM2.5 and its major constituents over the study period, using a theoretical minimal risk concentration of 0 μg/m3 as the reference because there is no evidence of any safe exposure threshold (Extended Method S2).

The following sensitivity analyses were conducted: (1) alternative time windows of PM2.5 and its major constituents; (2) including cities with complete 5-year outpatient visit data during 2013–2017; (3) replacing daily mean temperature with daily maximum or minimum temperature; and (4) adjusting for natural cubic splines (6 degrees of freedom) of moving averages of daily mean temperature and RH of the present day and previous 14 days [24,32].

All statistical analyses were conducted in R 4.0.3, using the survival package for conditional logistic regression and the metafor package for random-effects models. To compare the effect estimates across PM2.5 constituents, exposure windows, and outcome types, we performed pairwise comparisons using Wald tests, with Benjamini-Hochberg false discovery rate (FDR) correction for multiple comparisons. A two-tailed P-value < 0.05 was considered to be statistically significant.

3. Results

3.1. Reductions in ambient PM2.5 and its major constituents during 2013–2017

Across 274 cities at or above the prefecture level in China during 2013–2017, the overall mean concentrations of PM2.5 and its major constituents were 48.8 μg/m3 (PM2.5), 2.8 μg/m3 (BC), 10.5 μg/m3 (sulfate), 8.4 μg/m3 (nitrate), 6.2 μg/m3 (ammonium), 2.1 μg/m3 (chloride), and 18.7 μg/m3 (OM), with daily mean temperature and RH of 13.5 °C and 67.4%, respectively (Table S3). Compared to 2013, ambient PM2.5 and its major constituents showed gradual reductions in annual mean concentrations over the study period, with 2017 levels decreasing by 16.6 μg/m3 (28.7%) for PM2.5, 0.7 μg/m3 (20.5%) for BC, 2.9 μg/m3 (24.5%) for sulfate, 2.4 μg/m3 (24.8%) for nitrate, 1.6 μg/m3 (22.9%) for ammonium, 0.5 μg/m3 (21.9%) for chloride, and 8.4 μg/m3 (35.9%) for OM (Fig. 1 and Table S4). Geographically, higher PM2.5 levels were observed in the North China Plain and the Tarim Basin in Xinjiang (Fig. S1), with their major constituents showing distinct spatial patterns. BC, sulfate, nitrate, ammonium, chloride, and OM were all high in the North China Plain, while sulfate, chloride, and OM drove high PM2.5 in the Tarim Basin (Fig. S2).

Fig. 1.

Fig. 1

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Annual mean concentrations of ambient PM2.5 and its major constituents across 274 Chinese cities at or above the prefecture level during 2013–2017. Bars represent annual mean concentrations of PM2.5 and its major constituents (left axis). Lines with dots denote relative annual mean concentrations normalized to 2013 levels (set as 100%, right axis). BC, black carbon; OM, organic matter; PM2.5, fine particulate matter.

3.2. Associations between major PM2.5 constituents and cause-specific outpatient visits

A total of 212,086,796 cause-specific outpatient visit records in 274 Chinese cities at or above the prefecture level during 2013–2017 were included in this analysis. Short-term exposures to ambient PM2.5 and its major constituents showed statistically significant positive associations with outpatient visits for respiratory diseases across all time windows. In contrast, for cardiovascular, mental-neurological, and kidney diseases, the associations between PM2.5 and its major constituents and outpatient visits were predominantly significant at lag0, and the associations at lag0 were consistently higher than those observed in other time windows (Fig. S3). Specifically, for each IQR (31.7 μg/m3) increase in PM2.5 at lag0, the percent changes in outpatient visits for respiratory, cardiovascular, mental-neurological, and kidney diseases were 2.67% [95% confidence interval (CI): 2.35–3.00], 1.35% (95% CI: 1.06–1.64), 2.13% (95% CI: 1.70–2.56), and 0.83% (95% CI: 0.51–1.15), respectively. Based on the above results, lag0 was used as the primary time window for subsequent analyses.

Different major PM2.5 constituents exhibited generally similar association patterns with cause-specific outpatient visits as aforementioned, but the magnitudes of associations varied substantially. The associations with BC were the largest among the different major PM2.5 constituents for most major diseases investigated (Fig. 2). Specifically, for each IQR (1.8 μg/m3) increase in BC at lag0, the percent changes in cause-specific outpatient visits ranged from 1.33% (95% CI: 0.93–1.74) for CKD to 3.89% (95% CI: 3.30–4.49) for COPD. The other major PM2.5 constituents also demonstrated significant positive associations with outpatient visits for all the major diseases investigated in the single-constituent model, with ammonium showing a particularly strong association with outpatient visits for anxiety (2.60%; 95% CI: 1.62–3.59) (Fig. 2).

Fig. 2.

Fig. 2

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Percent changes in cause-specific outpatient risks associated with each interquartile range increase in ambient PM2.5 and its major constituents at lag0 based on the single-constituent model across 274 Chinese cities at or above the prefecture level during 2013–2017. The dots and lines represent the percent changes and 95% confidence intervals of the outpatient risks associated with each interquartile range increase in PM2.5 and its major constituents. CHD, coronary heart disease; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; LRI, lower respiratory infections.

Across all sensitivity analyses—including alternative temperature metrics (daily maximum/minimum), varied lag structures for meteorological variable adjustment (lag0-14), additional control for ozone, and substitution of ground-based meteorological data with the ERA5 data—the effect estimates for PM2.5 and its major constituents remained broadly consistent with those from the main model. These results indicate that our findings are robust and insensitive to alternative covariate choices, lag specifications, and exposure data sources (Fig. S4).

3.3. Black carbon as a major contributor to PM2.5-related outpatient risks

Among the major PM2.5 constituents, BC was identified as the primary constituent demonstrating the strongest and most consistent associations with increased outpatient visits for most of the major diseases investigated. In fact, the associations with BC were even stronger than those with PM2.5 per IQR increase, ranging from 1.13 times higher percent change in outpatient visits for anxiety to 1.81 times higher percent change in outpatient visits for CHD at lag0 in the single-constituent model (all P-values < 0.05 except for anxiety) (Fig. 2). The remaining PM2.5 mass-adjusted model and the constituent residual model further supported the independent positive associations between BC and outpatient visits for all the disease categories investigated (all P-values < 0.05) (Fig. S5). In the two-constituent model, the positive associations between BC and outpatient visits for all the disease categories remained robust after adjusting for all other major PM2.5 constituents separately, whereas the significant positive associations between the other major PM2.5 constituents and outpatient visits for most disease categories observed in the single-pollutant model no longer exist after adjusting for BC (Fig. S6).

Among all the major PM2.5 constituents, BC was associated with the largest attributable fractions of cause-specific outpatient visits for nearly all the major diseases (except for anxiety), with values ranging from 2.38% for CKD to 6.12% for COPD (Fig. 3). The annual attributable fractions of outpatient visits for 12 major diseases associated with PM2.5 and BC exhibited consistent reductions from 2013 to 2017 (Fig. 4). Specifically, absolute reductions in fractions of cause-specific outpatient visits attributable to PM2.5 and BC were 2.20% and 2.18% for respiratory diseases, 1.37% and 1.46% for cardiovascular diseases, 1.81% and 1.55% for mental-neurological diseases, and 0.97% and 0.86% for kidney diseases, respectively. In particular, the absolute reduction in the fraction of cardiovascular outpatient visits attributable to BC was larger than that attributable to PM2.5 (1.46% vs. 1.37%). Among the major diseases, the largest reductions in attributable fractions of outpatient visits were observed for cardiac arrhythmias (2.80% for PM2.5 and 2.41% for BC) (Fig. 4).

Fig. 3.

Fig. 3

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Attributable fractions of major cause-specific outpatient visits associated with ambient PM2.5 and its major constituents at lag0 across 274 Chinese cities at or above the prefecture level during 2013–2017. The attributable fractions for cause-specific outpatient visits associated with PM2.5 and its major constituents were evaluated based on the exposure-response associations obtained from the single-pollutant model with a theoretical minimal risk concentration of 0 μg/m3 as the reference.

Fig. 4.

Fig. 4

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Annual trends of attributable fractions of major cause-specific outpatient visits associated with ambient PM2.5 and black carbon at lag0 across 274 Chinese cities at or above the prefecture level during 2013–2017. The annual attributable fractions for cause-specific outpatient visits associated with PM2.5 and BC were evaluated based on the exposure-response associations obtained from a single-pollutant model with a theoretical minimal risk concentration of 0 μg/m3 as the reference.

3.4. Differences between black carbon-related risks and burdens of outpatient visits and hospital admissions

Among all the time windows, the strongest associations between BC and risks of outpatient visits and hospital admissions were most commonly observed at lag0 and lag0-1, respectively. As shown in Table 1, exposure-window sensitivity differed between BC-associated outpatient visit and hospital admission risks. For outpatient visits, the associations with BC at lag0 were mostly stronger than those at lag0-1, with the relative increases in percent changes ranging from 1.09 times higher for cardiac arrhythmias to 1.95 times higher for CHD (all P-values < 0.05 except for asthma, LRI, COPD, and cardiac arrhythmias). The only exception was LRI, where the percent change in outpatient visits at lag0-1 was 1.09 times higher than that at lag0, although statistically insignificant. In contrast, for hospital admissions, the associations with BC at lag0-1 were generally stronger than those at lag0, except for cardiac arrhythmias, heart failure, and stroke.

Table 1.

Percent changes in cause-specific outpatient risks associated with each interquartile range increase in black carbon at lag0 and lag0-1 based on the single-constituent model across 274 Chinese cities at or above the prefecture level during 2013–2017.

Disease Outpatient visits (%)
 Hospital admissions (%)a
lag0 lag0-1 lag0 lag0-1
Asthma 3.23 (2.58, 3.89) 2.76 (2.05, 3.47) 0.79 (−0.20, 1.79) 0.98 (−0.18, 2.16)
LRI 3.83 (3.42, 4.24) 4.17 (3.71, 4.64) 2.41 (1.86, 2.97) 2.63 (1.99, 3.27)
COPD 3.89 (3.30, 4.49) 3.75 (3.09, 4.42) - -
Cardiac arrhythmias 3.73 (2.92, 4.55) 3.44 (2.58, 4.30) 2.35 (1.65, 3.05) 2.34 (1.52, 3.16)
CHD 2.16 (1.75, 2.58) 1.11 (0.73, 1.50) 2.51 (2.04, 2.98) 2.61 (2.05, 3.17)
Heart failure 2.63 (2.00, 3.25) 1.96 (1.30, 2.63) 2.49 (1.69, 3.30) 2.27 (1.35, 3.19)
Stroke 3.19 (2.73, 3.65) 2.49 (1.99, 2.99) 2.61 (2.14, 3.08) 2.46 (1.92, 3.01)
Depression 2.37 (1.68, 3.07) 1.64 (1.01, 2.29) 4.98 (3.00, 7.00) 5.21 (2.91, 7.56)
Schizophrenia 3.30 (2.60, 4.01) 2.09 (1.35, 2.83) 3.16 (2.11, 4.22) 3.54 (2.34, 4.75)
Parkinson’s disease 2.17 (1.56, 2.79) 1.44 (0.83, 2.06) 2.84 (1.64, 4.05) 3.09 (1.68, 4.52)
Anxiety 2.49 (1.81, 3.18) 1.77 (1.10, 2.44) - -
CKD 1.33 (0.93, 1.74) 0.74 (0.30, 1.19) 3.54 (2.66, 4.42) 4.02 (3.06, 4.98)
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a

Data for hospital admissions were extracted from Supplementary Table S7 in our previous publication [24].

From 2013 to 2017, the declining concentrations of PM2.5 and BC were associated with differential reductions in the attributable fractions for outpatient visits and hospital admissions. For most major diseases, attributable fractions associated with PM2.5 and BC both declined more markedly for outpatient visits than for hospital admissions over the study period (Table 2). Among the major diseases with both outpatient visit and hospital admission data, the mean absolute reductions in annual attributable fractions for outpatient visits were 1.76% for PM2.5 and 1.57% for BC, both exceeding the corresponding reductions for hospital admissions (1.08% and 1.34%, respectively). Among the major diseases, cardiac arrhythmias showed the most pronounced differences between the absolute reductions in annual attributable fractions (from 2013 to 2017) of outpatient visits and hospital admissions (2.80% vs. 0.89% for PM2.5, and 2.41% vs. 1.04% for BC). The mean relative reductions in annual attributable fractions from 2013 to 2017 were 40.61% for PM2.5 and 28.66% for BC for outpatient visits, both exceeding the corresponding mean relative reductions for hospital admissions (30.00% and 21.14%, respectively). Among the major diseases, CKD showed the most pronounced differences between the relative reductions in annual attributable fractions (from 2013 to 2017) for outpatient visits and hospital admissions (46.61% vs. 24.53% for PM2.5, and 30.28% vs. 18.77% for BC).

Table 2.

Reductions in attributable fractions of cause-specific outpatient visits associated with ambient PM2.5 and black carbon at lag0 in 274 Chinese cities at or above the prefecture level in 2017 compared with 2013a,b.

Disease Outcome PM2.5
 BC
Absolute reduction (%)c Relative reduction (%)d Absolute reduction (%)c Relative reduction (%)d
Respiratory disease
 Asthma Outpatient visit 2.23 (1.73, 2.71) 40.44 (40.21, 40.67) 1.83 (1.48, 2.16) 28.74 (28.58, 28.91)
Hospital admissione - - - -
 LRI Outpatient visit 2.29 (2.02, 2.56) 39.65 (39.51, 39.78) 2.20 (1.98, 2.41) 28.84 (28.74, 28.95)
Hospital admissione 0.99 (0.67, 1.30) 31.39 (31.23, 31.55) 1.54 (1.19, 1.88) 27.01 (26.82, 27.20)
 COPD Outpatient visit 2.08 (1.73, 2.42) 36.16 (35.97, 36.34) 2.14 (1.84, 2.43) 28.26 (28.11, 28.41)
Hospital admissione - - - -
Cardiovascular disease
 Cardiac arrhythmias Outpatient visit 2.80 (2.06, 3.52) 44.96 (44.64, 45.28) 2.41 (1.92, 2.88) 33.07 (32.85, 33.30)
Hospital admissione 0.89 (0.51, 1.27) 32.52 (32.33, 32.72) 1.04 (0.69, 1.37) 22.79 (22.62, 22.98)
 CHD Outpatient visit 1.22 (0.92, 1.52) 41.15 (41.04, 41.27) 1.20 (0.98, 1.41) 26.16 (26.06, 26.26)
Hospital admissione 0.75 (0.50, 1.00) 29.74 (29.62, 29.87) 1.04 (0.83, 1.24) 19.67 (19.57, 19.78)
 Heart failure Outpatient visit 1.57 (1.17, 1.96) 37.10 (36.91, 37.29) 1.46 (1.13, 1.77) 27.58 (27.41, 27.74)
Hospital admissione 0.73 (0.33, 1.12) 28.92 (28.73, 29.11) 0.87 (0.53, 1.19) 18.90 (18.72, 19.08)
 Stroke Outpatient visit 2.07 (1.75, 2.38) 40.43 (40.30, 40.57) 1.79 (1.55, 2.02) 28.14 (28.03, 28.25)
Hospital admissione 0.80 (0.57, 1.02) 29.66 (29.55, 29.77) 0.92 (0.72, 1.10) 19.29 (19.19, 19.40)
Mental and neurological disease
 Depression Outpatient visit 1.87 (1.24, 2.48) 43.03 (42.76, 43.30) 1.41 (1.01, 1.79) 29.78 (29.59, 29.97)
Hospital admissione 2.00 (0.81, 3.09) 34.35 (33.81, 34.89) 2.67 (1.59, 3.63) 24.61 (24.03, 25.20)
 Schizophrenia Outpatient visit 1.82 (1.40, 2.22) 36.44 (36.23, 36.65) 1.75 (1.40, 2.09) 28.22 (28.05, 28.39)
Hospital admissione 1.62 (1.04, 2.17) 32.56 (32.29, 32.83) 1.54 (1.05, 2.00) 21.50 (21.25, 21.74)
 Parkinson’s disease Outpatient visit 1.22 (0.84, 1.60) 36.11 (35.92, 36.30) 1.07 (0.78, 1.35) 25.88 (25.74, 26.02)
Hospital admissione 0.91 (0.42, 1.39) 26.33 (26.06, 26.60) 1.05 (0.59, 1.48) 17.76 (17.51, 18.01)
 Anxiety Outpatient visit 2.03 (1.40, 2.64) 42.48 (42.20, 42.77) 1.41 (1.04, 1.76) 28.84 (28.66, 29.02)
Hospital admissione - - - -
Kidney disease
 CKD Outpatient visit 0.97 (0.60, 1.33) 46.61 (46.48, 46.74) 0.86 (0.61, 1.11) 30.28 (30.17, 30.40)
Hospital admissione 1.00 (0.72, 1.28) 24.53 (24.37, 24.69) 1.38 (1.08, 1.67) 18.77 (18.57, 18.97)
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a

The attributable fractions for cause-specific outpatient visits were calculated based on the exposure-response relationships with a theoretical minimal risk exposure level of 0 μg/m3 as the reference concentration.

b

Annual attributable fractions were calculated for cities with outpatient visit data in that year.

c

Compared with the annual outpatient visit attributable fraction in 2013 (or the earliest year with available outpatient data), the absolute reduction in outpatient visit attributable fraction in 2017 (or the latest year with available outpatient data) was calculated by subtracting the annual outpatient visit attributable fraction in 2017 (or the latest year) from the annual attributable fraction in 2013 (or the earliest year).

d

Compared with the annual outpatient visit attributable fraction in 2013 (or the earliest year), the relative reduction was calculated by dividing the absolute reduction in attributable fraction in 2017 or the latest year (with reference to the annual attributable fraction in 2013 or the earliest year) by the annual attributable fraction in 2013 or the earliest year.

e

Data for hospital admissions were extracted from Table 2 in our previous publication [24].

4. Discussion

This large-scale nationwide study used 212 million outpatient records from 274 Chinese cities during 2013–2017 to systematically investigate the associations between short-term exposures to ambient PM2.5 and its major constituents and outpatient visits for 12 major diseases across multi-organ systems. BC was identified as a major contributor to PM2.5-associated outpatient morbidity risks, supported by the strongest independent associations with and the highest attributable fractions for major cause-specific outpatient visits among major PM2.5 constituents. Notably, BC-associated morbidity risks showed distinct time window sensitivities for outpatient visits versus hospital admissions, with the strongest associations predominantly observed at lag0 for outpatient visits but at lag0-1 for hospital admissions, supporting outpatient visits as a sensitive morbidity indicator for early warning of acute health effects related to particulate air pollution. Since 2013, China’s clean air policies have led to gradual reductions in nationwide mean concentrations of PM2.5 and its major constituents during the study period, accompanied by reduced annual attributable fractions of PM2.5- and BC-associated outpatient visits for all 12 major diseases, demonstrating substantial health benefits from air quality improvements. Importantly, these reductions were more pronounced for outpatient visits than for hospital admissions, underscoring outpatient visits as a timely morbidity indicator for evaluating the effectiveness of air pollution control policies.

Notably, our study identified BC as the primary constituent driving PM2.5-associated acute outpatient morbidity. BC showed the strongest and most consistent positive associations across nearly all the major diseases, with the magnitudes of associations exceeding those of total PM2.5 mass per IQR increase. The independent associations of BC with cause-specific outpatient visits were further supported by the constituent residual model, the remaining PM2.5 mass-adjusted model, and the two-constituent model, and its positive associations with all the disease categories remained statistically robust after adjusting for the other major PM2.5 constituents. Conversely, while other major PM2.5 constituents also showed significant positive associations with outpatient visits for all the major diseases at lag0 in the single-constituent model, most associations became statistically insignificant after adjusting for BC in the two-constituent model. This finding suggests that the potential effects of these major constituents may depend on co-exposure with BC or be secondary to BC, further highlighting the dominant role of BC among the major PM2.5 constituents. The magnitudes of associations between short-term exposures to PM2.5 and its major constituents and cause-specific outpatient visits in our study are generally aligned with the existing literature [15,31]. While previous studies have also identified significant positive associations between short-term BC exposure and increased outpatient visits for pediatric acute respiratory infection [15], asthma [31], and depression [21], few have conducted a similar systematic and comprehensive analysis to investigate the robustness and independence of BC’s associations with cause-specific outpatient visits relative to other major PM2.5 constituents.

Quantification of the attributable fractions further supported BC’s pivotal role as a major contributor to PM2.5-related outpatient morbidity. Among the major PM2.5 constituents, BC was found to be associated with the largest attributable fractions of cause-specific outpatient visits for nearly all the major diseases. Following the implementation of the Air Pollution Prevention and Control Action Plan in China since 2013, the nationwide concentrations of PM2.5 and its major constituents declined gradually during 2013–2017. Correspondingly, the annual attributable fractions of all 12 major cause-specific outpatient visits associated with PM2.5 and BC decreased steadily over the study years. For respiratory, mental-neurological, and kidney diseases, absolute reductions in the fractions of outpatient visits attributable to PM2.5 were larger than those attributable to BC. In contrast, for cardiovascular diseases, the absolute reduction in the fraction of outpatient visits attributable to BC was larger than that attributable to PM2.5, suggesting that reducing BC may yield greater benefits than controlling total PM2.5 mass in alleviating early-stage cardiovascular morbidity burden.

Indeed, the unique physicochemical properties of BC (i.e., high specific surface area and porous structure) allow it to act as an effective carrier for co-occurring trace constituents (e.g., polycyclic aromatic hydrocarbons and heavy metals) [33], thereby amplifying its toxicity [34]. Synthesis of the existing toxicological evidence suggests that BC can induce a broad spectrum of adverse health effects via interrelated mechanisms, which collectively underpin its strong associations with all the major diseases across multiple organ systems. In addition to the canonical theories of oxidative stress and inflammation [[35], [36], [37]], three rapid-response mechanisms that operate within minutes to hours may better explain the same-day (lag0) associations between BC and outpatient visits for diseases affecting organs distant from the lungs, such as the heart, kidney, and brain. First, ultrafine BC particles can undergo rapid physical translocation. Particles deposited in the nasal epithelium can translocate along the olfactory nerve, reaching the olfactory bulb within one day after inhalation [38]. Particles deposited in the alveolar region can cross the alveolar-capillary barrier rapidly [39,40]. In rat models, 27 nm nanoparticles could enter the bloodstream within approximately 20 min after pulmonary administration, and particles < 34 nm are capable of rapid transepithelial translocation [39]. Once in the systemic circulation, these particles may reach the heart and coronary vasculature, where they can exert direct effects on vascular function and cardiac tissue [41]; they can also reach the kidneys—which receive approximately 25% of cardiac output—as confirmed by direct visualization of BC particles in human kidney tissues [42]. In addition, BC particles may compromise blood-brain barrier integrity [43], facilitating entry into various brain regions [44,45]. Second, BC deposition in the lung airways rapidly activates pulmonary sensory nerves, including C-fibers and rapidly adapting receptors, initiating vagal afferent reflexes that are integrated in the brainstem and result in a rapid shift toward sympathetic predominance [46,47]. This reflex pathway can be completed within minutes, as demonstrated by a controlled human exposure study in which diesel exhaust inhalation significantly increased muscle sympathetic nerve activity within 10 min, peaking at 30 min [41]. This sympathoexcitation can elevate blood pressure, alter cardiac electrophysiology, modify renal hemodynamics, and influence cerebral blood flow and autonomic regulation of heart rate—all within the acute minutes-to-hours period [41,45]. Third, once in the brain, BC particles can activate microglia within hours, triggering rapid neuroinflammatory responses and neurotransmitter dysregulation that may acutely affect cognitive function and behavior [48,49]. Together, these three interconnected mechanisms—rapid physical translocation (minutes to one day), neural reflex pathways (minutes), and rapid neuroinflammatory responses (hours)—may provide a temporally coherent and biologically plausible basis for the observed independent associations between BC and major cause-specific outpatient visits at lag0.

Prominent differences exist between BC-associated risks of outpatient visits and hospital admissions, particularly in terms of time window sensitivity. For outpatient visits, most major diseases showed stronger associations with BC at lag0 (immediate exposure time window) than at lag0-1 (cumulative exposure time window over two days), reflecting the immediate response of mild symptoms or acute exacerbations of chronic diseases after BC exposure. The only exception was LRI, which showed a stronger association with BC at lag0-1, likely due to its pathological process requiring cumulative air pollution exposure to induce obvious symptoms. In contrast, for hospital admissions, the associations with BC were generally stronger at lag0-1 than at lag0, indicating a potential delayed cumulative effect of BC. Exceptions were observed for cardiac arrhythmias, heart failure, and stroke, which showed stronger associations at lag0 than at lag0-1, possibly because these conditions are characterized by greater severity and thus require immediate medical intervention upon onset. The distinct patterns of time window sensitivity for BC-associated outpatient visit and hospital admission risks are particularly meaningful because outpatient visits—compared with more severe outcomes (e.g., hospital admissions or mortality)—better capture milder acute episodes or chronic disease exacerbations, thereby providing comprehensive insights into the acute morbidity risks potentially linked to PM2.5. Despite the statistically insignificant associations with hospital admissions for asthma, BC showed significant associations with outpatient visits for asthma, further supporting the greater sensitivity of outpatient visits in capturing air pollution-related morbidity risks than hospital admissions.

Furthermore, the attributable fractions associated with PM2.5 and BC during 2013–2017 declined more markedly for outpatient visits than for hospital admissions across most major diseases, highlighting the higher sensitivity of outpatient visits to changes in PM2.5 and BC concentrations compared with hospital admissions. This greater sensitivity may reflect the fact that outpatient visits capture milder, early-stage morbidity, which responds more promptly to air pollution, whereas hospital admissions reflect more severe, progressive conditions with less immediate responsiveness. The differential reductions in attributable fractions of outpatient visits and hospital admissions suggest that air pollution control measures could yield greater public health benefits in mitigating early-stage outpatient morbidity, highlighting that outpatient visits may serve as timelier morbidity indicators than hospital admissions for evaluating the effectiveness of air pollution control policies.

The present study has several strengths that enhance the validity and generalizability of the findings. First, the large-scale nationwide dataset of over 212 million outpatient records across 274 cities provides sufficient statistical power to detect subtle associations between PM2.5 and its major constituents and cause-specific outpatient visits, while also capturing geographical diversity in air pollution profiles and disease morbidity burdens. Second, the use of outpatient visits as the primary endpoint enables sensitive detection of mild acute effects and disease exacerbations related to air pollution, which is complementary to mortality and hospital admissions. Third, the use of the remaining PM2.5 mass-adjusted, constituent residual, and two-constituent models enabled the independent associations of individual major PM2.5 constituents (particularly BC) to be evaluated rigorously, minimizing the risk of confounding by mixture exposure. Finally, the investigation of a broad spectrum of diseases across multiple organ systems, including understudied mental-neurological and kidney diseases, provides a comprehensive understanding of the potential acute health impacts of PM2.5 and its major constituents.

Nevertheless, several limitations should be acknowledged. First, as with all observational time-series analyses, residual time-varying confounding cannot be fully excluded, precluding any definitive causal interpretations of the observed BC-outpatient visit associations. Second, the use of anonymized city-level outpatient visit data precluded differentiation between initial visits and follow-up visits (e.g., pre-scheduled medication refills or regular appointments), which may inflate the estimated risks of acute morbidity. However, the proportion of such routine follow-up visits usually remains relatively stable over time, constituting background variation that is unlikely to significantly affect the observed associations between air pollution and outpatient risks. The use of city-level exposure data also carries a risk of exposure misclassification, although city-level exposure data align with common practices for developing air pollution control policies and related public health intervention strategies. Third, despite our extensive efforts to validate outpatient diagnoses through a combination of automated fuzzy matching and expert chart review, the inherent variability in diagnostic terminology across hospitals and physicians may still lead to outcome misclassification. However, any such non-differential error would most likely attenuate the true associations, rendering our findings conservative rather than spurious. Finally, the absence of health data from rural populations, who may experience different PM2.5 compositions and exposure levels and may have distinct health vulnerabilities, limits the generalizability of the study findings. Our study is also restricted by the lack of data from 2018 onward, as medical insurance records covering 2018 and subsequent years have not yet been made accessible to researchers for academic use. Even so, our findings still provide valuable evidence regarding morbidity risks linked to PM2.5 exposure, especially for this pivotal period of air-pollution policy implementation in China.

5. Conclusions

In conclusion, this nationwide study of 274 Chinese cities evaluated the acute outpatient risks and burdens attributable to short-term exposures to PM2.5 and its major constituents, filling a key evidence gap for early morbidity outcomes. BC was identified as a major contributor to PM2.5-associated outpatient risks and burdens for respiratory, cardiovascular, mental-neurological, and kidney diseases, highlighting notable heterogeneity in the morbidity risks associated with different major PM2.5 constituents that bulk PM2.5 mass-based assessments may not capture. As further reducing total PM2.5 mass becomes increasingly challenging in the future, our findings may inform a strategic shift from sole mass control to constituent-targeted regulation prioritizing key PM2.5 constituent(s) (e.g., BC), to improve the efficiency and cost-effectiveness of public health protection policies. Furthermore, we have demonstrated that outpatient visit may be a sensitive and timely morbidity indicator to detect acute morbidity risks related to air pollution, offering a valuable early-warning tool complementary to mortality or hospital admission-based surveillance. Beyond direct policy implications, our findings provide an empirical basis for future research on spatiotemporal patterns and biological mechanisms of PM2.5 constituent-specific health effects, and inform the refinement of evidence-based air quality guidelines worldwide.

CRediT authorship contribution statement

Yang Lan: Writing – review & editing, Writing – original draft, Visualization, Validation, Investigation, Funding acquisition, Conceptualization. Jian Lei: Writing – review & editing, Visualization, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Huimeng Liu: Writing – review & editing, Validation, Methodology, Investigation. Yunxing Jiang: Writing – review & editing, Validation, Investigation, Funding acquisition. Chenlu Yang: Writing – review & editing, Investigation. Qinchi Yu: Writing – review & editing, Investigation. Jinxi Wang: Writing – review & editing, Data curation. Chen Wei: Writing – review & editing, Data curation. Yinxiang Li: Writing – review & editing, Data curation. Jing Wei: Writing – review & editing, Data curation. Xi Chen: Writing – review & editing, Data curation. Furong Deng: Writing – review & editing. Xinbiao Guo: Writing – review & editing. Yuewei Liu: Writing – review & editing, Supervision. Shaowei Wu: Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

All authors declare no competing interests.

Acknowledgements

The study is supported by grants from the National Natural Science Foundation of China (82273587, 82525058, 42407589, 82203991), Shaanxi Province Natural Science Basic Research Program (2024JC-YBMS-757), Fundamental Research Funds for Central Universities (xzy012026120, xpt012025012, xzy012026119), and China Postdoctoral Science Foundation (2023TQ0260). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the paper.

Footnotes

Appendix A

Supplementary file to this article can be found online at https://doi.org/10.1016/j.eehl.2026.100283.

Contributor Information

Yuewei Liu, Email: liuyuewei@mail.sysu.edu.cn.

Shaowei Wu, Email: shaowei_wu@xjtu.edu.cn.

Appendix A. Supplementary data

The following is the Supplementary file to this article:

Multimedia component 1
mmc1.pdf (2.6MB, pdf)

References

  • 1.GBD 2023 Disease and Injury and Risk Factor Collaborators Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet Lond. Engl. 2025;406:1873–1922. doi: 10.1016/S0140-6736(25)01637-X. 10513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Feng Y., Castro E., Wei Y., Jin T., Qiu X., Dominici F., et al. Long-term exposure to ambient PM2.5, particulate constituents and hospital admissions from non-respiratory infection. Nat. Commun. 2024;15(1):1518. doi: 10.1038/s41467-024-45776-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Krall J.R., Mulholland J.A., Russell A.G., Balachandran S., Winquist A., Tolbert P.E., et al. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ. Health Perspect. 2017;125(1):97–103. doi: 10.1289/EHP271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Lei J., Chen R., Liu C., Zhu Y., Xue X., Jiang Y., et al. Fine and coarse particulate air pollution and hospital admissions for a wide range of respiratory diseases: a nationwide case-crossover study. Int. J. Epidemiol. 2023;52(3):715–726. doi: 10.1093/ije/dyad056. [DOI] [PubMed] [Google Scholar]
  • 5.Dominici F., Peng R.D., Bell M.L., Pham L., McDermott A., Zeger S.L., et al. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA. 2006;295(10):1127–1134. doi: 10.1001/jama.295.10.1127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Tian Y., Ma Y., Wu J., Wu Y., Wu T., Hu Y., et al. Ambient PM2.5 chemical composition and cardiovascular disease hospitalizations in China. Environ. Sci. Technol. 2024;58(37):16327–16335. doi: 10.1021/acs.est.4c05718. [DOI] [PubMed] [Google Scholar]
  • 7.Wei Y., Feng Y., Danesh Yazdi M., Yin K., Castro E., Shtein A., et al. Exposure-response associations between chronic exposure to fine particulate matter and risks of hospital admission for major cardiovascular diseases: population based cohort study. BMJ Clin. Res. Ed. 2024;384 doi: 10.1136/bmj-2023-076939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Bai L., Jiang Y., Wang K., Xie C., Yan H., You Y., et al. Ambient air pollution and hospitalizations for schizophrenia in China. JAMA Netw. Open. 2024;7(10) doi: 10.1001/jamanetworkopen.2024.36915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Lee S., Lee W., Kim D., Kim E., Myung W., Kim S.Y., et al. Short-term PM2.5 exposure and emergency hospital admissions for mental disease. Environ. Res. 2019;171:313–320. doi: 10.1016/j.envres.2019.01.036. [DOI] [PubMed] [Google Scholar]
  • 10.Yang T., Wang J., Huang J., Kelly F.J., Li G. Long-term exposure to multiple ambient air pollutants and association with incident depression and anxiety. JAMA Psychiatry. 2023;80(4):305–313. doi: 10.1001/jamapsychiatry.2022.4812. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Li G., Huang J., Wang J., Zhao M., Liu Y., Guo X., et al. Long-term exposure to ambient PM2.5 and increased risk of CKD prevalence in China. J. Am. Soc. Nephrol. 2020;32(2):448–458. doi: 10.1681/ASN.2020040517. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Liang K.H., Colombijn J.M.T., Verhaar M.C., Ghannoum M., Timmermans E.J., Vernooij R.W.M. The general external exposome and the development or progression of chronic kidney disease: a systematic review and meta-analyses. Environ. Pollut. 2024;358 doi: 10.1016/j.envpol.2024.124509. [DOI] [PubMed] [Google Scholar]
  • 13.Wu D., Zheng H., Li Q., Wang S., Zhao B., Jin L., et al. Achieving health-oriented air pollution control requires integrating unequal toxicities of industrial particles. Nat. Commun. 2023;14:6491. doi: 10.1038/s41467-023-42089-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Jin L., Xie J., Wong C.K.C., Chan S.K.Y., Abbaszade G., Schnelle-Kreis J., et al. Contributions of city-specific fine particulate matter (PM2.5) to differential in vitro oxidative stress and toxicity implications between Beijing and Guangzhou of China. Environ. Sci. Technol. 2019;53(5):2881–2891. doi: 10.1021/acs.est.9b00449. [DOI] [PubMed] [Google Scholar]
  • 15.Xu H., Song J., He X., Guan X., Wang T., Zhu Y., et al. Ambient anthropogenic carbons and pediatric respiratory infections: a case-crossover analysis in the megacity Beijing. GeoHealth. 2023;7(8) doi: 10.1029/2023GH000820. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Xu R., Zhang P., Wu T., Liu H., Wang F., Peng J., et al. Association of acute exposure to PM2.5 constituents and sources with kidney injury: a longitudinal panel study of Nrf2 promoter polymorphism. J. Hazard. Mater. 2024;480 doi: 10.1016/j.jhazmat.2024.136141. [DOI] [PubMed] [Google Scholar]
  • 17.Zhou P., Hu J., Yu C., Bao J., Luo S., Shi Z., et al. Short-term exposure to fine particulate matter constituents and mortality: case-crossover evidence from 32 counties in China. Sci. China Life Sci. 2022;65(12):2527–2538. doi: 10.1007/s11427-021-2098-7. [DOI] [PubMed] [Google Scholar]
  • 18.World Health Organization . 2021. Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. [PubMed] [Google Scholar]
  • 19.Moon J., Kim E., Jang H., Song I., Kwon D., Kang C., et al. Long-term exposure to PM2.5 and mortality: a national health insurance cohort study. Int. J. Epidemiol. 2024;53(6) doi: 10.1093/ije/dyae140. [DOI] [PubMed] [Google Scholar]
  • 20.Wang C., Feng L., Chen K. The impact of ambient particulate matter on hospital outpatient visits for respiratory and circulatory system disease in an urban Chinese population. Sci. Total Environ. 2019;666:672–679. doi: 10.1016/j.scitotenv.2019.02.256. [DOI] [PubMed] [Google Scholar]
  • 21.Zhuang Z., Li D., Zhang S., Hu Z., Deng W., Lin H. Short-term exposure to PM2.5 chemical components and depression outpatient visits: a case-crossover analysis in three Chinese Cities. Toxics. 2024;12(2):136. doi: 10.3390/toxics12020136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.GBD 2021 Causes of Death Collaborators, Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Lond. Engl. 2024;403:2100–2132. doi: 10.1016/S0140-6736(24)00367-2. 10440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.GBD 2021 Diseases and Injuries Collaborators Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2133–2161. doi: 10.1016/S0140-6736(24)00757-8. 10440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Liu H., Lei J., Liu Y., Zhu T., Chan K., Chen X., et al. Hospital admissions attributable to reduced air pollution due to clean-air policies in China. Nat. Med. 2025;31(5):1688–1697. doi: 10.1038/s41591-025-03515-y. [DOI] [PubMed] [Google Scholar]
  • 25.Lee D.-C., Wang J., Shi L., Wu C., Sun G. Health insurance coverage and access to care in China. BMC Health Serv. Res. 2022;22(1):140. doi: 10.1186/s12913-022-07498-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Wei J., Li Z., Chen X., Li C., Sun Y., Wang J., et al. Separating daily 1 km PM2.5 inorganic chemical composition in China since 2000 via deep learning integrating ground, satellite, and model data. Environ. Sci. Technol. 2023;57(46):18282–18295. doi: 10.1021/acs.est.3c00272. [DOI] [PubMed] [Google Scholar]
  • 27.Muñoz-Sabater J., Dutra E., Agustí-Panareda A., Albergel C., Arduini G., Balsamo G., et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data. 2021;13(9):4349–4383. [Google Scholar]
  • 28.Lawrence M.G. The relationship between relative humidity and the dewpoint temperature in moist air: a simple conversion and applications. Bull. Am. Meteorol. Soc. 2005;86(2):225–233. [Google Scholar]
  • 29.Gu J., Shi Y., Zhu Y., Chen N., Wang H., Zhang Z., et al. Ambient air pollution and cause-specific risk of hospital admission in China: a nationwide time-series study. PLoS Med. 2020;17(8) doi: 10.1371/journal.pmed.1003188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Rai M., Breitner S., Wolf K., Peters A., Schneider A., Chen K. Future temperature-related mortality considering physiological and socioeconomic adaptation: a modelling framework. Lancet Planet. Health. 2022;6(10):e784–e792. doi: 10.1016/S2542-5196(22)00195-4. [DOI] [PubMed] [Google Scholar]
  • 31.Jung C.-R., Young L.-H., Hsu H.-T., Lin M.-Y., Chen Y.-C., Hwang B.-F., et al. PM2.5 components and outpatient visits for asthma: a time-stratified case-crossover study in a suburban area. Environ. Pollut. 2017;231:1085–1092. doi: 10.1016/j.envpol.2017.08.102. [DOI] [PubMed] [Google Scholar]
  • 32.Ye X., Wolff R., Yu W., Vaneckova P., Pan X., Tong S. Ambient temperature and morbidity: a review of epidemiological evidence. Environ. Health Perspect. 2012;120(1):19–28. doi: 10.1289/ehp.1003198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Wang J., Liu D., Ge X., Wu Y., Shen F., Chen M., et al. Characterization of black carbon-containing fine particles in Beijing during wintertime. Atmos. Chem. Phys. 2019;19(1):447–458. [Google Scholar]
  • 34.Hakkarainen H., Salo L., Mikkonen S., Saarikoski S., Aurela M., Teinilä K., et al. Black carbon toxicity dependence on particle coating: measurements with a novel cell exposure method. Sci. Total Environ. 2022;838 doi: 10.1016/j.scitotenv.2022.156543. [DOI] [PubMed] [Google Scholar]
  • 35.Cheng W., Zhang W., Xia X., Zhang J., Wang M., Li Y., et al. The domino effect in inhaled carbon black nanoparticles triggers bloodbrain barrier disruption via altering circulatory inflammation. Nano Today. 2023;48 [Google Scholar]
  • 36.Cheng Z., Chu H., Wang S., Huang Y., Hou X., Zhang Q., et al. TAK1 knock-down in macrophage alleviate lung inflammation induced by black carbon and aged black carbon. Environ. Pollut. 2019;253:507–515. doi: 10.1016/j.envpol.2019.06.096. [DOI] [PubMed] [Google Scholar]
  • 37.Shang Y., Xue W., Kong J., Chen Y., Qiu X., An X., et al. Ultrafine black carbon caused mitochondrial oxidative stress, mitochondrial dysfunction and mitophagy in SH-SY5Y cells. Sci. Total Environ. 2022;813 doi: 10.1016/j.scitotenv.2021.151899. [DOI] [PubMed] [Google Scholar]
  • 38.Oberdörster G., Sharp Z., Atudorei V., Elder A., Gelein R., Kreyling W., et al. Translocation of inhaled ultrafine particles to the brain. Inhal. Toxicol. 2004;16(6–7):437–445. doi: 10.1080/08958370490439597. [DOI] [PubMed] [Google Scholar]
  • 39.Choi H.S., Ashitate Y., Lee J.H., Kim S.H., Matsui A., Insin N., et al. Rapid translocation of nanoparticles from the lung airspaces to the body. Nat. Biotechnol. 2010;28(12):1300–1303. doi: 10.1038/nbt.1696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Zhang Y., Zhang Q., Yao Z., Li H. Particle size and mixing state of freshly emitted black carbon from different combustion sources in China. Environ. Sci. Technol. 2020;54(13):7766–7774. doi: 10.1021/acs.est.9b07373. [DOI] [PubMed] [Google Scholar]
  • 41.Rankin G.D., Kabéle M., Brown R., Macefield V.G., Sandström T., Bosson J.A. Acute exposure to diesel exhaust increases muscle sympathetic nerve activity in humans. J. Am. Heart Assoc. 2021;10(10) doi: 10.1161/JAHA.120.018448. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Rasking L., Koshy P., Bongaerts E., Bové H., Ameloot M., Plusquin M., et al. Ambient black carbon reaches the kidneys. Environ. Int. 2023;177 doi: 10.1016/j.envint.2023.107997. [DOI] [PubMed] [Google Scholar]
  • 43.Drew L. Air pollution and brain damage: what the science says. Nature. 2025;637(8046):536–538. doi: 10.1038/d41586-025-00053-y. [DOI] [PubMed] [Google Scholar]
  • 44.Oppenheim H.A., Lucero J., Guyot A.C., Herbert L.M., McDonald J.D., Mabondzo A., et al. Exposure to vehicle emissions results in altered blood brain barrier permeability and expression of matrix metalloproteinases and tight junction proteins in mice. Part. Fibre Toxicol. 2013;10:62. doi: 10.1186/1743-8977-10-62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Vanbrabant K., van Dam D., Bongaerts E., Vermeiren Y., Bové H., Hellings N., et al. Accumulation of ambient black carbon particles within key memory-related brain regions. JAMA Netw. Open. 2024;7(4) doi: 10.1001/jamanetworkopen.2024.5678. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Perez C.M., Hazari M.S., Farraj A.K. Role of autonomic reflex arcs in cardiovascular responses to air pollution exposure. Cardiovasc. Toxicol. 2015;15(1):69–78. doi: 10.1007/s12012-014-9272-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Widdicombe J., Lee L.Y. Airway reflexes, autonomic function, and cardiovascular responses. Environ. Health Perspect. 2001;109(Suppl 4):579–584. doi: 10.1289/ehp.01109s4579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Block M.L., Wu X., Pei Z., Li G., Wang T., Qin L., et al. Nanometer size diesel exhaust particles are selectively toxic to dopaminergic neurons: the role of microglia, phagocytosis, and NADPH oxidase. FASEB J. 2004;18(13):1618–1620. doi: 10.1096/fj.04-1945fje. [DOI] [PubMed] [Google Scholar]
  • 49.Kang Y.J., Tan H.-Y., Lee C.Y., Cho H. An air particulate pollutant induces neuroinflammation and neurodegeneration in human brain models. Adv. Sci. 2021;8(21) doi: 10.1002/advs.202101251. [DOI] [PMC free article] [PubMed] [Google Scholar]

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