Source apportionment and driving mechanisms of black carbon in a mountainous megacity: insights from urban–suburban observations in Chongqing, China

Abstract

Black carbon, a strongly light-absorbing aerosol from incomplete combustion, has raised global concern due to its substantial impacts on climate forcing, air quality degradation, and public health risks. This study investigates the distribution and sources of equivalent BC (eBC) across urban–suburban sites in Chongqing, a mountainous megacity in southwestern China, based on year-long multi-wavelength Aethalometer (AE33) observations. Four sites were deliberately selected to represent distinct source regimes—port, airport, rail freight, and dense urban traffic—in a spatial source separation design. Annual mean eBC concentrations ranged from 2.4 ± 1.3 µg m⁻³ at the urban site to 2.8 ± 1.9 µg m⁻³ in suburban areas. Using absorption Ångström exponent diagnostics, eBC was apportioned into traffic-related (eBC_liquid) and biomass/coal-burning (eBC_solid) components. Results showed distinct spatiotemporal patterns between the two components: eBC_liquid dominated urban area (up to 85%) and was significantly elevated during weekdays (p < 0.0001), while eBC_solid exhibited a pronounced wintertime enhancement in suburban areas, accounting for nearly 40% of total eBC, indicating the influence of seasonal solid fuel combustion. Seasonally resolved correlation analysis further revealed that source–tracer relationships, such as the eBC_liquid–AAE correlation at the port site, shifted markedly with season, capturing the transition from shipping-dominated emissions in summer to mixed solid fuel influences in winter. Notably, riverine freight activity during the flood season contributed to episodic increases in eBC_liquid. Local meteorological factors, especially wind and temperature, substantially modulated eBC accumulation and dispersion. Strong correlations with NO_x, SO₂, and NMHCs further improved the source attribution. These findings underscore the heterogeneous behaviors of eBC components under complex terrain–meteorology interactions and provide region-specific evidence for emission control. The results suggest that strengthening traffic management in urban cores and promoting clean energy substitution in suburban areas are essential to reduce BC pollution and improve air quality in mountainous megacities.

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Environmental significance

Black carbon (BC) is a key short-lived climate pollutant and health-relevant aerosol that remains insufficiently characterized in rapidly urbanizing and topographically complex regions. This study provides process-level insights into BC source attribution and behavior across an urban–suburban gradient in Chongqing, a mountainous megacity in southwestern China. Our year-long, multi-site analysis reveals how traffic, biomass combustion, and riverine freight activities shape BC dynamics under seasonal meteorological modulation. These findings identify spatially and temporally specific intervention windows for pollution control, informing evidence-based strategies in terrain-influenced urban environments.

1 Introduction

Black carbon (BC), a strongly light-absorbing aerosol primarily emitted from incomplete combustion (e.g., fossil fuels and biofuel), has attracted global attention due to its significant impacts on climate warming and public health deterioration.¹ As the second most potent climate forcer after CO₂, BC contributes substantially to atmospheric warming through direct radiative forcing and snow/ice albedo reduction.^2,3 It is also a key component of PM_2.5 and has been strongly associated with adverse health outcomes, including respiratory, cardiovascular, and neurological disorders.^4,5 BC also worsens haze by suppressing vertical pollutant dispersion.^6,7 Its morphology and surface area promote aerosol interactions and reactive chemistry.^8–10 Recognized as a short-lived climate pollutant (SLCP) by the World Health Organization¹¹ and the Climate and Clean Air Coalition,¹² BC is a critical target for mitigation efforts that deliver near-term co-benefits for both climate stabilization and air quality improvement. Comprehensive BC monitoring remains vital in densely populated regions with complex sources.

China, as one of the largest global BC emitters,^13,14 faces persistent challenges in balancing pollution control and economic growth. While national policies have led to notable reductions in PM_2.5 concentrations in recent years,^15,16 the carbonaceous fractions in PM_2.5 (BC, BrC, and OC) remain high in urban areas,^17,18 as water-soluble inorganic fractions decline.^19,20 These carbonaceous components originate from diverse sources including road traffic, riverine freight, residential combustion of coal and biomass, and secondary organic aerosol (SOA) formation from volatile organic compounds (VOCs).²¹ In many cities, transportation accounts for over 70% of urban BC, underscoring liquid fossil fuel dominance in densely populated regions.^22–25 Chongqing, the largest city in western China, exemplifies these issues. Its high BC emissions are exacerbated by complex terrain, frequent stagnation, and meteorological inversions, leading to pollutant accumulation.^14,26–28 Dense transportation sources (e.g., road-traffic, riverine freight traffic) and seasonal combustion (e.g., biomass burning, meat curing) compound pollution.^29–31 Moreover, frequent stagnation enhances BC aging and absorption, complicating source attribution.^32,33

Although BC has been widely studied in megacities,^34–39 multi-site observations remain scarce in topographically complex regions like southwestern China.⁴⁰ Mountain-basin meteorology limits atmospheric mixing, driving spatial heterogeneity in BC levels.⁴¹ Local meteorological drivers (wind, temperature) affecting BC are underexplored.^42,43 To address these gaps, we conducted multi-site measurements across an urban–suburban transect in Chongqing to examine BC dynamics and their response to meteorology and emissions. Specifically, we aimed to: (1) identify temporal patterns of BC and its sources, (2) evaluate meteorological influences, and (3) assess co-pollutant correlations to refine source attribution.

BC can be termed as elemental carbon (EC), refractory black carbon (rBC), or equivalent black carbon (eBC), depending on the measurement method.⁴⁴ In this study, we refer to BC as eBC, which is calculated by converting the light absorption coefficient into mass concentration using a suitable mass absorption cross-section (MAC) applied by our measurement equipment. This study provides empirical insights into eBC behavior across urban–suburban areas under complex terrain and meteorology. The findings highlight the contrasting behaviors of black carbon components in response to emission types and meteorological conditions, revealing their distinct spatiotemporal signatures. These insights can support differentiated mitigation strategies, such as targeted control on vehicular emissions year-round, and seasonal restrictions on residential biomass combustion and port-related emissions. Overall, the study advances region-specific understanding of BC and offers policy-relevant insights for air quality and climate co-management.

2 Methods

2.1 Monitoring sites and collection periods

Field measurements were conducted from January to December 2024 at four sites (HJ, GY, JB, TJ) in metropolitan Chongqing (Fig. 1), classified as urban (HJ) or suburban (GY, JB, TJ) based on land use. HJ is located in central Chongqing, surrounded by dense traffic and residential areas. GY, JB, and TJ are located near the GuoYuan port, Jiangbei Airport, and TuanJie Railway freight station, respectively. These sites were deliberately selected to represent distinct source regimes—port/shipping (GY), airport (JB), rail freight (TJ), and dense urban traffic (HJ)—constituting a “spatial source separation” approach that enables a source-oriented comparison of BC concentrations across different urban microenvironments. Notably, the so-called “suburban” sites (GY, TJ, JB) are situated in peripheral zones where industrial and logistical facilities are concentrated—a direct consequence of stringent environmental policies that prohibit coal combustion and restrict high-emission activities within the Chongqing metropolitan core. While these locations reflect different local emission influences, they may not represent city-wide eBC levels. Establishing monitoring sites at cleaner background locations, better suited as regional reference points, is planned in future studies.

Fig. 1 Geographic distribution of the study area and sampling sites. (a) Location of Chongqing in China. (b) Topographic map of Sichuan Basin, Southwest China. (c) Land cover types in the central urban area of Chongqing based on GL30 data, with dark red areas indicating “artificial surfaces” (i.e., surfaces resulting from human construction activities). (d)–(g) Satellite images of the surroundings of JB, TJ, HJ and GY site, respectively.

2.2 eBC measurement

eBC concentrations were measured using 7-wavelength aethalometers (Model-AE33, Magee Scientific), operating at 5 L min⁻¹ with a PM_2.5 cyclones. Optical attenuation (ATN) and the absorption coefficient (b_abs) at 370–950 nm were calculated using assumed wavelength-dependent MAC values and a multiple-scattering correction factor (C_ref).⁴⁵ A dual-spot algorithm corrected filter-loading artifacts.⁴⁶ ATN at 880 nm was converted to eBC using MAC = 7.77 m² g^{−1 44}. C_ref was set to 2.919, calculated as C₀·H = 1.39 × 2.1, where C₀ is the manufacturer-recommended value for the “8060” filter tape in this study,⁴⁷ and H is a harmonization factor.^48,49

2.3 Auxiliary measurements

All auxiliary measurements were conducted simultaneously with BC at each of the four sites. PM₁₀ and PM_2.5 were measured using hybrid real-time monitors (5030i SHARP, Thermo Scientific). Gaseous pollutants (O₃, CO, SO₂, NO₂) were also monitored using Thermo analyzers (models 49i, 48i, 43i, 42i). Non-methane hydrocarbons (NMHCs) were quantified by an online GC-FID (EXPEC 2000). Meteorological data including relative humidity (RH), temperature (AT), pressure (AP), wind speed (WS), and wind direction (WD) were obtained from an automatic meteorological station (Lufft WS500, Hach).

2.4 Reanalysis and emission inventory

Hourly planetary boundary layer height (BLH) were retrieved from ERA5 reanalysis of European Centre for Medium-Range Weather Forecasts (ECMWF) at 0.25° resolution (https://doi.org/10.24381/cds.adbb2d47, accessed on: Jun 16, 2025). BC emission data were derived from the Global Emission Modeling System (GEMS),⁵⁰ covering global emissions of key pollutants (e.g., CO₂, NO_x, SO₂, PM, BC, OC, PAHs) from 1960–2019 at 0.1° resolution (https://gems.sustech.edu.cn, accessed on: June 24, 2025). Given that the spatial distribution of global BC emission hotspots is generally stable over short timescales, the 2019 GEMS inventory remains representative for the present study. Gridded BC emissions from the Multi-resolution Emission Inventory for China (MEIC v2.0, https://meicmodel.org.cn, accessed on: May 9, 2026) at 0.25° × 0.25° resolution for the year 2023 were also used for regional-scale comparison in the SI (Fig. S15). Land cover data were obtained from GlobeLand30 (30 m resolution), developed by the National Geomatics Center of China (NGCC, https://www.webmap.cn/mapDataAction.do?method=globalLandCover; accessed on January 21, 2025), used to identify urban, vegetation, water, and other surface types.

2.5 Aethalometer model

The aethalometer model⁵¹ apportions BC based on the wavelength dependence of light absorption. The b_abs is modeled as a sum of solid (e.g., biomass, coal) and liquid fuel (e.g., traffic-related) emissions following a power law: b_abs (λ) ∼ λ^−AAE, where λ is wavelength and AAE is the Ångström exponent.^52,53 AAE values were set to 1 and 2 for liquid and solid fuels, respectively.^4,54–58

AAE values from biomass and coal combustion are typically higher than those from traffic (excluding heavy fuel oil).^59–61 The original model assumes negligible coal combustion, allowing BC partitioning into fossil fuel and biomass burning sources.^47,62,63 However, coal consumption remains a major BC source in China,^64–66 especially in Beijing–Tianjin–Hebei region and southwest provinces.⁶⁷ BC source apportionment was therefore estimated as follow:

b_abs(470 nm) = b_abs(470 nm)_liquid + b_abs(470 nm)_solid (1)

b_abs(950 nm) = b_abs(950 nm)_liquid + b_abs(950 nm)_solid (2)

BC_solid = BC(880 nm) − BC_liquid (6)

3 Results and discussion

3.1 Overview of eBC concentration

eBC concentrations varied modestly across the sites, with consistently higher levels at suburban locations. Annual averages followed the order: TJ (3.04 ± 2.07 µg m⁻³) > GY (2.95 ± 1.79 µg m⁻³) > JB (2.49 ± 1.62 µg m⁻³) > HJ (2.39 ± 1.31 µg m⁻³), consistent with observations in other megacities such as Los Angeles,⁶⁸ Madrid,⁶⁹ Milan,⁷⁰ Shanghai,⁷¹ Beijing.^72,73 This trend reflects growing emissions in peri-urban areas amid reduced urban core pollution. As illustrated in Fig. 2, BC concentrations in Chinese cities are generally higher than those reported in Europe,^47,70,74,75 the United States,^68,76 and Japan,⁷⁷ but lower than those observed in India,^78,79 despite differences in observation periods and instrumentation.

Fig. 2 BC concentrations in various megacities globally. The bar charts represent BC concentrations in both urban and suburban areas. The purple bars indicate concentrations in urban areas, while the grey bars represent suburban areas. The map in the center shows the global distribution of BC emissions from the GEMS inventory. Note that observation periods vary among the cited studies (see Table S1 for details).

Among Chinese cities, Chongqing exhibited slightly higher average eBC concentrations compared to southern coastal cities such as Shanghai, Shenzhen, and Guangzhou^{71–73,80,81} (see Table S1 for additional comparisons). Notably, winter eBC at an urban site in 2016 reached 7.2 µg m⁻³,⁸² 2 to 3 times higher than current winter values (3.2 ± 4.7 µg m⁻³), likely due to stricter air quality policies in recent years.⁸³

The eBC_liquid concentrations (Fig. S1a) followed the order GY ≈ TJ > HJ > JB, while eBC_solid (Fig. S1b) was highest at TJ and JB and lowest at HJ. This demonstrates that liquid fossil fuel emissions dominate at all sites. The HJ site exhibited the highest proportion of liquid fuel (83.6 ± 6.2%) as shown in Fig. S1c, reflecting the significant influence of traffic emissions near roadways. Additionally, Fig. S2 showed that HJ had the highest levels of NMHC and NO_x, both of which are indicators of vehicular emissions.^84,85 Conversely, HJ recorded the lowest SO₂ concentration, while suburban sites exhibited considerably higher levels. These elevated concentrations underscore the substantial impact of coal or other heavy fuel combustion at these sites.^86,87 These contrasting urban–suburban patterns reflect the stringent emission controls enforced within the Chongqing metropolitan core (e.g., coal ban, vehicle restrictions), which confine solid-fuel and industrial sources to peripheral zones.

The AAE also provides insight into aerosol sources. In this study, the AAE was derived by curve fitting a power function to the full spectral wavelength range. As depicted in Fig. S1d, the AAE value at the urban HJ site (1.19 ± 0.1) was notably lower than those at the suburban sites: JB (1.41 ± 0.2), TJ (1.35 ± 0.1), and GY (1.32 ± 0.2). A high AAE generally reflects an increased wavelength dependence of aerosol light absorption, indicating the presence of additional absorbing components beyond “pure” BC, particularly brown carbon (BrC) or aged organic matter.⁸⁸ Biomass burning typically produces high AAE values ranging from 1.2 to 2.9 due to BrC emissions under low combustion efficiency.^89–91 Residential coal combustion also contributes more organic carbon and BrC, resulting in higher AAE values ranging from 1.5 to 2.2.⁶⁰ In contrast, industrial combustion systems (e.g., coal-fired power plants) operate at high efficiency and temperature, which removes much of the organic matter, yielding AAE values close to or even below 1. Furthermore, gasoline-fueled combustion typically emits “pure” BC with minimal BrC, leading to lower AAE values (0.8 to 1.3) compared to biomass or heavy fuel combustion.^59,60,92 The notably lower AAE at HJ is therefore fully consistent with the predominance of gasoline-fueled traffic emissions inferred from the eBC liquid fraction and trace gas profiles.

Beyond these site-averaged characteristics, the differences in AAE observed among the sites in this study are likely attributable to variations in dominant emission sources, which aligns with the source apportionment results from the aethalometer model. Interestingly, as shown in Fig. 3, the proportions of eBC_liquid and eBC_solid exhibited minimal variations when the eBC concentrations were low at the sites. However, as eBC concentrations increased, the trends in the proportions of eBC_liquid and eBC_solid became inconsistent. At the GY site, the proportion of eBC_liquid increased by 16% with rising eBC concentrations. At the TJ and JB sites, the proportions of eBC_liquid decreased by 19% and 8%, respectively, while eBC_solid accounted for nearly 50% at higher eBC concentrations. At the HJ site, the proportion of eBC_liquid remained relatively stable, fluctuating between 86% and 83%. This differential behavior suggests that the composition of emission sources at the sites varies under different eBC concentration levels, which is further supported by the changes in AAE shown in Fig. 3. At the GY site, the AAE rapidly decreased with increasing eBC concentration, approaching 1, suggesting a significant contribution from liquid fuel combustion emissions at higher BC levels—a pattern consistent with shipping emissions given the site's proximity to GuoYuan Port (see also Section 3.2.1 for cargo throughput analysis). At the HJ site, the hourly averaged AAE remained between 1.1 and 1.3 across all eBC concentration bins, with no systematic decrease during high-BC episodes. This indicates that the relative contribution of solid fuel combustion (e.g., domestic heating) does not increase appreciably at this urban location, reinforcing the interpretation of a relatively stable influence from traffic-related emissions. The average AAE values at the TJ and JB sites were higher (ranging from 1.3 to 1.6), and a noticeable upward trend in AAE was observed as eBC concentrations increased. This suggests that these two sites are influenced by a combination of liquid and solid fuel combustion emissions. As eBC concentrations rise, the proportion of solid fuel emissions increases, which may be indicative of biomass burning or coal combustion emissions, prevalent in the Sichuan Basin during the autumn (SON) and winter (DJF) seasons.^93,94 In the subsequent sections, we will discuss the spatiotemporal characteristics of the changes in eBC_liquid and eBC_solid concentrations and the corresponding changes in emission source at each site.

Fig. 3 Averaged contributions of eBC_liquid and eBC_solid as a function of eBC concentration, along with box (25th–75th percentile) and whisker (5th–95th percentile) plots of AAE at each site. (a) GY, (b) TJ, (c) JB, (d) HJ sites.

3.2 Temporal characteristics

3.2.1 Monthly and seasonal variations. Further analysis of the daily averaged concentrations of eBC_liquid and eBC_solid at each site (Fig. 4) revealed that eBC_liquid concentrations ranged from 0.24 to 6.52 µg m⁻³,while eBC_solid concentrations ranged from 0.048 to 5.32 µg m⁻³. Time series data show that eBC_solid concentrations tend to display stronger seasonal patterns across the various sites compared to eBC_liquid. This discrepancy is attributed to the fact that urban traffic flow exhibits less seasonal variation than the seasonal emissions from solid fuel combustion (e.g., domestic coal combustion and biomass burning), with the former being more prevalent at most sites.⁴⁷ Generally, aerosol concentrations (e.g., PM_2.5 and BC) reached their highest levels during wintertime at most sites, as shown in Fig. S12a and b, primarily due to the seasonal variations in meteorological conditions (e.g., lower BLH and WS) and emission sources (e.g., domestic heating and meat fumigation).^23,29,95,96 The monthly variations of BLH and WS at each site are shown in Fig. S3. And the evidence for increased emission intensity from solid fuel combustion in autumn and winter is provided by our previous study.⁹⁴

Fig. 4 Time series of eBC_liquid, eBC_solid, and AAE at each site. (a) GY, (b) TJ, (c) JB, (d) HJ sites.

It is worth noting that, despite the relatively stable seasonal variations in eBC_liquid across the sites, the contribution of eBC_liquid at GY showed a notable increase from May to September 2024, as illustrated in Fig. 4a and S12d. During this period, concentrations were nearly 30% higher compared to the rest of the year. This seasonal enhancement is likely related to the site's proximity to the freight port in the city, where shipping activity on the Yangtze River typically intensifies during the flood season. As shipping companies schedule more voyages to take advantage of elevated water levels and improved navigability, the observed increase in eBC_liquid may be attributed to enhanced emissions from inland waterway transport. In fact, this increase in activity is supported by the recent cargo throughput data for Chongqing Guoyuan Port (Table S2), which further demonstrates the elevated port activities during the flood season. In contrast, shipping volume generally declines during the dry season, potentially leading to lower contributions outside the flood period.^97,98 Moreover, Fig. S12e showed that the proportion of eBC_liquid at the HJ site consistently remained above 75%, indicating a strong influence from vehicular emissions based on its monthly variation. Meanwhile, at suburban sites (GY, JB, and TJ), the contribution of eBC_liquid decreased from approximately 80% in spring (MAM) and summer (JJA) to around 60% in autumn and winter. This decline can be attributed to enhanced seasonal emission sources, such as coal and wood burning for domestic heating, as well as the traditional meat fumigatetion (e.g., sausage and bacon) in Southwest China, as mentioned earlier.

Another point to note is that the monthly average AAE at the urban HJ site exhibited a relatively narrow fluctuations, primarily between 1.1 and 1.3, indicating a dominant influence from vehicular emissions. In comparison, suburban sites (GY, JB, and TJ) exhibited greater variability in AAE, ranging from 1.1 to 1.6, suggesting the presence of additional sources (i.e., solid fuel combustion) during winter. Overall, AAE demonstrated a pronounced seasonal pattern, with lower values in spring and summer and higher values in autumn and winter. This seasonal trend is likely driven by both changes in emission sources and meteorological conditions, particularly reduced atmospheric dispersion and frequent stagnation events in autumn and winter, which facilitate the aerosols aging. Aged aerosols typically exhibit higher AAE values, as indicated by several studies.^99,100

3.2.2 Diurnal patterns and weekend effect. Diurnal plots (Fig. 5) were used to validate whether the temporal variation patterns of the two eBC components aligned with the expected behavior of their respective emission sources. In general, the diurnal variation of aerosol pollutants (e.g., BC) is primarily driven by atmospheric dispersion conditions (e.g., BLH and WS), with a typical pattern of lower concentrations during the daytime and higher concentrations at nighttime. At the urban site HJ, eBC_liquid displayed a distinct morning peak at 07:00 (local time, LT), associated with rush hour emissions, followed by a decline as BLH increased. Subsequently, a smaller peak emerged around 20:00 LT, coinciding with reduced atmospheric mixing and enhanced surface accumulation in the evening. As illustrated in Fig. S4a, the eBC_liquid at HJ exhibited a pattern similar to NO_x, indicating the prevailing influence of vehicular emissions. This bimodal distribution was also observed at JB and TJ. The first peak typically occurred prior to the development of the boundary layer and was attributed to the accumulation of traffic-related emissions under stagnant conditions. As BLH deepened and WS strengthened, enhanced vertical and horizontal dispersion led to a reduction in eBC_liquid concentrations. The evening rebound coincided with weakened atmospheric mixing. In contrast, eBC_solid variations were more strongly influenced by dispersion than by source activity. Its levels remained stable before BLH growth and declined notably as daytime mixing intensified.

Fig. 5 Mean hourly variations in eBC_liquid and eBC_solid, as well as meteorological parameters (WS and BLH) across the sites.

Notably, eBC_liquid at the GY site exhibited a distinct nighttime unimodal pattern. As discussed in Section 3.2.1, port activities near the GY site intensify during the flood season (May–September), resulting in elevated eBC_liquid concentrations. This suggests that the observed nighttime peak is closely linked to port-related operations. A study using VIIRS satellite nighttime light data across 601 global anchorages demonstrated substantial nocturnal port activity, particularly during high-volume shipping periods.¹⁰¹ Similarly, Murata¹⁰² employed CE-SAT-IIB nighttime satellite imagery to assess operations at multiple terminals in Tokyo Port, revealing elevated nighttime activity during high-demand shipping periods. In addition, long-term noise monitoring outside ports has also confirmed frequent night operations, especially during peak periods.¹⁰³

Previous studies^33,55,75,104 have reported a “weekend effect” in carbonaceous aerosols, primarily attributed to variations in anthropogenic emissions between weekdays and weekends. To further evaluate the consistency between the expected behavior of eBC emissions and the observed spatiotemporal variation patterns, we analyzed the diurnal variations in eBC concentrations across all sites for each day of the week (Fig. 6).

Fig. 6 Mean hourly variations in eBC_liquid and eBC_solid for each day of the week across the sites. (a) HJ, (b) JB, (c) GY, (d) TJ sites.

At HJ, the weekday morning peak of eBC_liquid disappeared on weekends, and the evening peak was notably reduced, indicating a significant decline in local traffic-related activities during weekends. Unlike HJ, located in a typical urban center and sensitive to commuter-related traffic changes, the JB and TJ sites are adjacent to an airport and a railway freight station, respectively. As a result, although the bimodal patterns of eBC_liquid at JB and TJ were also attenuated on weekends, the decrease was less pronounced. This difference indicates that transportation activities around JB and TJ are less sensitive to the weekday/weekend differences due to continuous traffic flows, thus reducing the weekend effect. At the GY site, nighttime freight operations at the nearby port remain the dominant factor influencing eBC_liquid concentrations. While elevated concentrations were observed throughout the week, Fig. 6c reveals a reduction in nighttime levels from Saturday night to Sunday daytime, implying a temporary decline in port activity. However, this decrease was not statistically significant, as confirmed by the t-test results presented later.

For eBC_solid, no evident fluctuations between weekdays and weekends were observed across sites, suggesting that its emission sources (e.g., biomass burning, residential coal combustion) remained relatively stable throughout the week. This limited temporal variability contrasts with the more pronounced fluctuations in eBC_liquid, highlighting the distinct responses of the two components to temporal changes. Consistent with this, previous studies^105,106 reported similar negligible weekday/weekend differences at sites near industrial areas or rural suburbs.

Additionally, an independent samples t-test was conducted to asess the “weekend effect” (Fig. S5). Statistically significant differences in eBC_liquid were found at the HJ (p < 0.0001), JB (p < 0.01), and TJ (p < 0.01) sites, whereas no significant difference was detected at the GY site. Although a reduction in nighttime eBC_liquid was observed at GY, it did not reach the 0.01 significance level, indicating that the “weekend effect” at GY was not pronounced enough. No statistically significant differences in eBC_solid were detected at any of the sites at the 0.01 significance level.

3.3 Wind dependence of eBC concentrations

Wind rose plots (Fig. S6) indicated that hourly wind speeds were generally low across all sites, with suburban locations experiencing slightly higher values than the urban site. The hourly average WS followed the order: JB (1.3 ± 0.8 m s⁻¹) > TJ (1.0 ± 0.8 m s⁻¹) > GY (0.9 ± 0.4 m s⁻¹) > HJ (0.6 ± 0.4 m s⁻¹). The overall low WS are consistent with the weak ventilation typical of Chongqing's complex terrain,²⁶ while the lower values at the urban site likely reflect enhanced surface roughness from dense buildings and street canyons.¹⁰⁷ However, WD showed significant variability, suggesting that the spatial heterogeneity of emission sources influences site-specific eBC concentrations. To identify local BC sources, bivariate polar plots^108,109 were employed. As shown in Fig. 7a, high eBC concentrations generally occurred under low WS (<1 m s⁻¹), implying a dominant role of local sources and poor dispersion.

Fig. 7 Polar plots of eBC_liquid and eBC_solid for the sites (a) HJ, (b) JB, (c) GY, and (d) TJ.

At HJ, high eBC_liquid concentrations were linked to southern and northern winds, aligning with nearby traffic routes, while eBC_solid peaked under northeasterly winds. Despite these directional preferences, the two components showed minor differences in WD, indicating well-mixed pollution. Seasonal variability was limited but followed a slight north–south gradient (Fig. S7a and b). To further investigate the variability in emission sources, we established pairwise robust regression surface relationships¹¹⁰ between eBC_liquid and eBC, and between eBC_solid and eBC. Fig. 8a reveals that under southerly and southwesterly winds, nearly all eBC is present in the form of eBC_liquid, with a proportion approaching 0.8, primarily due to traffic emissions. Easterly winds showed significant variability, indicating regional contributions from biomass or coal combustion. Seasonal variations in the robust slopes (Fig. S8a and b) confirmed persistent traffic contributions under westerly and southwesterly winds, with values remaining between 0.75 and 0.9. Meanwhile, eBC_solid emissions from northerly winds occurred mainly in autumn and winter, as discussed in Section 3.2.1, suggesting that solid fuel combustion sources from the north can still impact the central urban area during specific periods.

Fig. 8 Polar plots of the robust regression slope between eBC_liquid and eBC, and between eBC_solid and eBC, using pairwise statistics, at (a) HJ, (b) JB, (c) GY, and (d) TJ.

At JB, high eBC concentrations were predominantly observed under calm northerly winds. While directional differences between eBC_liquid and eBC_solid were minor, regression slopes (Fig. 8b) revealed spatial heterogeneity: eBC_liquid dominated westward (traffic), whereas eBC_solid peaked east and south, likely due to biomass burning or residential coal use. The southeastern sector of JB is influenced by airport emissions, where aviation fuels with high sulfur content are used. Such fuels can produce aerosols with elevated AAE values.⁶⁰ In addition, low-temperature combustion during aircraft ground operations has been reported to yield AAE values >3.5.^111,112 These high-AAE aerosols may thus cause systematic overestimation of eBC_solid when applying the aethalometer model in this region.

At TJ, eBC was strongly WD-dependent. Both eBC_liquid and eBC_solid peaked under NW winds, with eBC_solid rising at higher WD, likely from a nearby freight yard. eBC_liquid dominated at WD < 6 m s⁻¹ (proportion > 0.65), while eBC_solid prevailed at higher WD (Fig. 8c), suggesting distinct source regimes. Fig. S8 showed high eBC_solid under strong NW winds in autumn and spring, coinciding with regional biomass burning peaks.^30,94

At GY, spatial patterns diverged: eBC_liquid was higher in the SE, eBC_solid in the NE. Summer eBC_liquid peaks occurred under low WD (Fig. S8), likely from nearby port operations which discussed in Section 3.2.2. Notably, the site uniquely showed a summer annual maximum, emphasizing port activity's role. Meanwhile, eBC_solid peaked in winter from the north, similar to other sites. Robust regression (Fig. 8d) confirmed distinct source zones: SE (port) for eBC_liquid, NE (biomass/coal) for eBC_solid.

Extending this wind-dependent analysis to AAE (Fig. S13 and S14) reveals additional site-specific features: at JB and GY, AAE decreases with increasing wind speed, consistent with the dilution of local solid fuel sources. In contrast, higher wind speeds at TJ and HJ are accompanied by elevated AAE, pointing to the transport of solid-fuel-rich or aged aerosols from upwind regions,⁹⁹ which further corroborates the source influences identified above.

A comparison of gridded BC emission inventories over the study region is provided in Fig. S15. According to both the GEMS and MEIC inventories,¹¹³ TJ, JB, and HJ are located within or immediately adjacent to high-BC-emission grid cells, consistent with the elevated eBC concentrations at these sites. The GY site, by contrast, falls near the edge of the high-emission zone in both inventories. This likely reflects the limited spatial resolution of current publicly available inventories, as well as the fact that the GuoYuan Port was still under construction when the GEMS 2019 inventory was compiled (the port was not fully completed until 2022). The MEIC 2023 inventory, although more recent, has a coarser resolution that may also not fully resolve localized shipping emissions. This comparison underscores the complementary value of ground-level monitoring in characterizing emissions from rapidly evolving, seasonally modulated sources that gridded inventories may overlook.

3.4 Relationships between the pollutants

Pollutant relationships at each site were evaluated using Spearman correlation matrics with hierarchical clustering (Fig. 9). PM_2.5 showed strong positive correlations (R_s > 0.7) with both eBC_solid and eBC_liquid at most sites, except GY, where eBC_liquid had a lower correlation (R_s = 0.43). This indicates eBC concentrations increased concurrently with elevated regional aerosol pollution, aligning with previous studies.^10,114–116 While both eBC components rose with PM_2.5 at most sites (Fig. S9), eBC_liquid at GY lacked a clear upward trend—likely due to intensified port activity during the flood season (Fig. 4 and S12d). Additionally, as PM_2.5 pollution worsened, both AAE and the fraction of eBC_solid increased. This reflects seasonal enhancement of emission sources, particularly from residential coal and biomass burning during autumn and winter, as reported by Zhang⁶⁷ and Mousavi⁶⁸ also reflects aerosol aging, which increases AAE through enhanced long-wavelength absorption.^99,100

Fig. 9 Correlation matrixs between the pollutants at each site. (a) HJ, (b) JB, (c) GY, (d) TJ sites.

eBC_liquid showed strong correlations with NMHC across sites, especially at TJ and HJ, indicating a strong gasoline combustion influence. The weaker correlation at JB may be attributed to its distance from major roads (>1 km) and the high reactivity of NMHC.^117,118 Additionally, eBC_liquid showed strong correlations with NO_x at all sites, underscoring the impact of traffic-related emissions. However, at HJ, despite the highest NO_x levels (Fig. S2), its correlation with eBC_liquid was weakest (R_s = 0.43). This may be due to complex urban emissions and enhanced photochemistry, where NO and O₃ participate in a reversible equilibrium,¹¹⁹ making NO_x an ambiguous indicator of primary sources. eBC_solid showed stronger correlations with SO₂ and AAE, indicating contributions from high-sulfur-content fuel like coal and heavy fuel oil.^60,92 Biomass burning could not be fully assessed due to the lack of specific tracers (e.g., K⁺, levoglucosan), but its typical high-AAE signature¹¹¹ may partially explain the observed enhancement in AAE and its association with eBC_solid. This highlights the need for future measurements—including ions, inorganic elements, and organic molecular markers—to better characterize source processes and distinguish the optical properties of specific emission sources in this region. The spatial source separation framework established in this study can inform the selection of tracer species and receptor sites for formal PMF/CMB analyses in such future campaigns.

Ambient temperature (AT) was inversely correlated with eBC, especially eBC_solid, reflecting increased heating-related emissions under lower temperatures.^60,89 However, this trend was absent at GY, likely due to high summer eBC_liquid from port activity. As shown in Fig. S10 and S11, eBC_solid concentrations peaked within the 8 to 18 °C range. While JB, TJ, and HJ also exhibited elevated eBC_liquid levels in this range, their temperature sensitivity was comparatively weak. In contrast, the eBC_liquid at GY site peaked at 24 to 36 °C. Fig. S11 further reveals that at GY, JB, and HJ, eBC_solid was more prevalent under lower temperatures, while eBC_liquid increased with higher temperatures. However, at TJ, no clear temperature dependence was observed, likely due to steady industrial/solid fuel emissions,¹⁰⁵ unlike the more seasonally driven patterns at other sites.⁹⁶

To further assess the seasonal stability of these pollutant relationships, the Spearman correlation analysis was repeated for each meteorological season at each site (Fig. S16–S19). Several seasonal patterns emerged consistently. First, correlations involving eBC_solid, particularly those with SO₂, AAE, and AT, were strongest in autumn and winter, reflecting enhanced solid fuel combustion and biomass burning during colder months. Second, eBC_liquid correlations with NMHC remained strong and stable year-round at all sites, consistent with the persistent contribution of traffic emissions. Third, the GY site exhibited a notable seasonal departure: the eBC_liquid–AAE correlation shifted from strongly negative in summer (R_s ≈ −0.60) to positive in winter (R_s ≈ 0.40), capturing the transition from shipping-dominated emissions during the flood season to a mixed influence of solid fuel combustion in winter. Similarly, the eBC_liquid–PM_2.5 correlation at GY weakened substantially in summer, consistent with the decoupling of port-related emissions from the regional aerosol background. These seasonal correlation patterns, consistent with the source-specific temporal variations discussed in Sections 3.2 and 3.3, indicate that the relationships among pollutants are not entirely stable across seasons; rather, they vary meaningfully with season and source activity.

3.5 Uncertainties of the aethalometer source apportionment

Several sources of uncertainty should be considered when interpreting the aethalometer-derived source apportionment results. First, the model assumes fixed AAE values for liquid (AAE ≈ 1) and solid (AAE ≈ 2) fuel combustion. While these values are widely adopted in the literature,^59,60 the actual AAE of specific sources can deviate due to differences in fuel composition, combustion conditions, and atmospheric aging. The reported source contributions should therefore be regarded as semi-quantitative estimates. A systematic sensitivity analysis—varying the assumed AAE values within plausible ranges—would help quantify the impact of this uncertainty on the apportioned contributions and represents a useful direction for future refinement of these results. Second, aerosol aging can increase AAE through enhanced long-wavelength absorption,^99,100 potentially leading to an overestimation of the solid fuel fraction for aged aerosols. This may partially explain the elevated AAE observed at the HJ site under higher wind speeds (Fig. S13). Third, the two-component model cannot resolve sub-categories within each fuel type (e.g., biomass burning vs. residential coal, or traffic vs. shipping). At JB, the influence of airport emissions with potentially high AAE values^111,112 introduces additional uncertainty in the eBC_solid estimates. Despite these limitations, the consistency between the aethalometer results and independent tracers (NMHC, NO_x, SO₂, Fig. S2 and 9), the physically interpretable seasonal and diurnal patterns (Fig. 4–6), and the site-specific differences that align with known source distributions (Fig. 7 and 8), collectively support the robustness of the main conclusions drawn from this analysis.

4 Conclusions

This study systematically characterized the spatiotemporal patterns and source contributions of eBC and its combustion components across an urban–suburban gradient in Chongqing, a megacity in a mountainous basin. A deliberate “spatial source separation” monitoring design, in which sites were selected to represent distinct emission regimes (port, airport, rail freight, and dense urban traffic), enabled a source-oriented assessment of BC pollution in this complex terrain.

Annual mean eBC concentrations ranged from 2.38 to 3.04 µg m⁻³, with slightly higher levels in suburban areas. This urban–suburban contrast reflects the combined effect of stringent emission controls within the metropolitan core—which confine coal combustion and high-emission industrial activities to peripheral zones—and the proximity of suburban sites to major logistical and industrial sources. Winter peaks were mainly driven by residential heating and unfavorable meteorological conditions, reflecting the sensitivity of BC pollution to seasonal energy demand and climate-related meteorological extremes.

Source apportionment based on AAE diagnostics indicated that liquid fuel combustion (e.g., traffic) dominated eBC, particularly in urban areas (up to 83.6%), while solid fuel combustion (e.g., biomass, coal) contributed more in suburban regions during autumn and winter, highlighting the importance of promoting clean energy transitions in peri-urban areas. Diurnal and weekly trends of eBC_liquid reflected traffic and port activity influences at HJ and GY, respectively. A seasonal reversal in the eBC_liquid–AAE relationship at GY—from strongly negative in summer to positive in winter—further captured the transition from shipping-dominated emissions during the flood season to a mixed influence of solid fuel combustion in winter. In contrast, eBC_solid exhibited stronger seasonal variation, reflecting episodic combustion. These findings highlight that seasonal river freight activity can significantly enhance BC levels, suggesting the need to consider shipping-related emissions in regional air quality management. A significant weekend decline in eBC_liquid further confirmed its traffic origin, while eBC_solid remained unaffected.

Correlation analysis with gaseous pollutants and meteorological parameters differentiated source types: eBC_liquid was strongly associated with NO_x and NMHCs, while eBC_solid correlated with SO₂ and AAE, suggesting high-sulfur fuel combustion. Seasonally resolved correlation matrices further revealed that while certain source–tracer pairs (e.g., eBC_liquid–NMHC) remained stable year-round, others varied meaningfully with season—underlining the value of time-resolved analysis in assessing tracer reliability. Negative correlations with temperature supported heating-related contributions. Wind analysis showed that cold, stagnant conditions promoted eBC accumulation, with source regions varying by site and season, underscoring how terrain–meteorology interactions may intensify air pollution episodes under climate variability. A comparison with gridded emission inventories (GEMS and MEIC) confirmed that the sites generally lie within high BC emission zones, while also highlighting that current inventories do not fully resolve localized, seasonally modulated sources such as port operations—demonstrating the complementary value of ground-level monitoring.

The contrasting behaviors of eBC_liquid and eBC_solid highlight the value of component-resolved BC analysis in identifying source-specific impacts and related health risks. The spatial source separation framework demonstrated here provides a practical and transferable monitoring strategy for other complex urban environments seeking to evaluate the influence of specific emission sources with relatively modest instrumentation. Our findings emphasize the need for year-round traffic emission control, alongside seasonal restrictions on shipping and biomass burning. These results provide a basis for targeted air quality strategies in complex urban–suburban settings, with implications for rapidly urbanizing regions globally.

Conflicts of interest

There are no conflicts to declare.

Data availability

The datasets supporting the conclusions of this article are available at the Mendeley Data repository through https://doi.org/10.17632/dkh855f28z.1. Data includes daily averaged concentrations of equivalent black carbon (eBC) and its source-apportioned fractions (eBC_liquid from fossil fuel, eBC_solid from biomass/coal), co-pollutants (NO_x, SO₂, NMHCs), and key meteorological parameters (temperature, wind speed) for one urban and one suburban site in Chongqing, China, covering the experiment period. Due to data use agreements with the Chongqing Municipal Bureau of Ecology and Environment, the underlying high-frequency (e.g., 1 minute) raw instrument data are not publicly shared. The datasets deposited in Mendeley data are sufficient to reproduce all the figures and statistical analyses presented in this article.

Supplementary information (SI): two tables and nineteen figures. Table S1 provides a global comparison of black carbon concentrations in megacities. Table S2 provides GuoYuan Port cargo throughput data. Fig. S1–S19 present detailed supplementary statistical analyses including box plots, polar plots, wind roses, scatter plots, and seasonal correlation matrices of eBC components, co-pollutants, meteorological parameters, and emission comparisons across all study sites. See DOI: https://doi.org/10.1039/d6ea00019c.

Acknowledgements

This study was funded by the National Key Research and Development Program of China (No. 2023YFC3709303 and No. 2023YFC3709302), the Chongqing Key Projects for Technological Innovation and Application Development (No. CSTB2022TIAD-CUX0008), and the Environmental Monitoring Scientific Research Development Fund of Chongqing Ecological Environment Monitoring Center (No. CQHJ-NBKY-2023-001). We gratefully acknowledge the ECMWF, NGCC, and SUSTech for providing the ERA5 dataset, GL30 dataset, and GEMS emission inventory data used in this study. We would like to express our sincere gratitude to David Carslaw (University of York) and Stuart Grange (Queensland University of Technology) for developing the “openair” package in R, which was essential to this study. We would also like to thank Cheng Wu at Jinan University for generously providing the open-source software for graphic drawing. We also thank Xiao Han (Institute of Atmospheric Physics, Chinese Academy of Sciences) for helpful discussions on emission inventory data, and Yanyun He (Magee Scientific) for expert advice on AE33 instrument uncertainties.

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Footnote

† These authors contributed equally to this paper.

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