Measurement report: quantifying source contribution of fossil fuels and biomass-burning black carbon aerosol in the southeastern margin of the Tibetan Plateau

Anthropogenic emissions of black carbon (BC) aerosol are transported from Southeast Asia to the southwestern Tibetan Plateau (TP) during the pre-monsoon; however, the quantities of BC from different anthropogenic sources and the transport mechanisms are still not well constrained because there have been no high-time-resolution BC source apportionments. Intensive measurements were taken in a transport channel for pollutants from Southeast Asia to the southeastern margin of the TP during the pre-monsoon to investigate the influences of fossil fuels and biomass burning on BC. A receptor model that coupled multi-wavelength absorption with aerosol species concentrations was used to retrieve site-specific Ångström exponents (AAEs) and mass absorption cross sections (MACs) for BC. An “aethalometer model” that used those values showed that biomass burning had a larger contribution to BC mass than fossil fuels (BC_biomass=57 % versus BC_fossil=43 %). The potential source contribution function indicated that BC_biomass was transported to the site from northeastern India and northern Burma. The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) indicated that 40 % of BC_biomass originated from Southeast Asia, while the high BC_fossil was transported from the southwest of the sampling site. A radiative transfer model indicated that the average atmospheric direct radiative effect (DRE) of BC was +4.6 ± 2.4 W m⁻², with +2.5 ± 1.8 W m⁻² from BC_biomass and +2.1 ± 0.9 W m⁻² from BC_fossil. The DRE of BC_biomass and BC_fossil produced heating rates of 0.07 ± 0.05 and 0.06 ± 0.02 K d⁻¹, respectively. This study provides insights into sources of BC over a transport channel to the southeastern TP and the influence of the cross-border transportation of biomass-burning emissions from Southeast Asia during the pre-monsoon.

1 Introduction

The Tibetan Plateau (TP) is an important regulator of climate change in the Northern Hemisphere, and it plays a crucial role in the functions of the global ecosystem and climate stability (Liu et al., 2019, 2020a). The TP is covered by one of the largest ice masses on Earth, and it has been called the water tower of Asia (Liu et al., 2020b). The glaciers on the TP are facing rapid retreat, however, and if unchecked, this could result in adverse effects on the Asian hydrological cycle and Asian monsoon (Luo et al., 2020; Hua et al., 2019). In spring, the glaciers on the TP begin to melt as part of the natural hydrological cycle, but the increasing quantities of black carbon (BC) aerosol transported to the TP have accelerated this process (Bond et al., 2013) by causing a warming effect in atmosphere over the TP and enhancing the absorption of radiation on the surface of the glaciers (Ming et al., 2009).

The southern part of the TP is bounded by South Asia, where air pollution often is severe (Chan et al., 2017). Several studies have shown that pollutants (including BC) from South Asia can be transported to the south of the TP along mountain valleys, especially during the pre-monsoon (March–May), when southwesterly winds prevail (e.g., Cao et al., 2010; Xia et al., 2011; Zhu et al., 2017; Niu et al., 2017). For example, Xia et al. (2011) analyzed satellite data and air mass trajectories and found that the TP, particularly the southern TP, was affected by pollutants carried by southwesterly winds from nearby regions in South Asia. In addition, numerous studies have shown that the high bulk BC mass loadings and the associated regional influences on the TP are related to transport from South Asia (Liu et al., 2015; Han et al., 2020; Cong et al., 2015; Wang et al., 2015). Nonetheless, assessments of regional transport of bulk BC aerosol have not fully revealed the impacts of different BC emission sources because the optical properties and radiative effects of BC can not only vary among sources in complex ways but can also be affected by aging during transport (Tian et al., 2019; Zhang et al., 2019). Therefore, quantitative information on the contributions of different sources of BC over the TP is lacking, but it is critically needed for a better understanding of the influence of anthropogenic emissions on its environment and climate.

Several studies have assessed the contributions of different BC sources through model simulations or isotopic methods. For example, Zhang et al. (2015) investigated BC sources for different parts of the TP by using a chemical transport model and a source-tagging approach, and they found that the contributions of BC sources varied among regions and with the seasons. Li et al. (2016) used filter sampling and carbon isotopes (Δ¹⁴C and δ¹³C) to determine the BC from fossil fuels and biomass burning in several areas of the TP. A major disadvantage of filter-based measurements is that they are constrained by low time resolution, which makes it challenging to capture the detailed evolution of pollution events. On the other hand, the accuracy of model simulations is dependent on many factors, including uncertainties associated with initial particle parameters, aging processes, the accuracy of emission inventory, meteorological fields over the complex terrain, the modules for chemistry and planetary boundary layer (PBL) dynamics, etc. (Koch et al., 2009; Madala et al., 2014; Vignati et al., 2010). Nonetheless, these studies have been helpful for understanding the sources of BC over the TP.

To make up for the deficiencies of filter-based analysis, BC source apportionments based on high-time-resolution online data have been conducted in many locations (e.g., Herich et al., 2011; Zhu et al., 2017; Rajesh and Ramachandran, 2018) but for the TP are limited. An “aethalometer model” based on multi-wavelength absorption data is one efficient approach for distinguishing between BC from fossil fuel and biomass-burning sources (Sandradewi et al., 2008). The accuracy of the aethalometer model relies on the input parameters, including absorption Ångström exponent (AAE) and BC mass absorption cross section (MAC_BC) of different sources (Zotter et al., 2017). Limited information on site-specific Ångström exponents (AAEs) and MAC_BCs lead most studies to rely on values taken from measurements made in other locations (e.g., Healy et al., 2017; Zhu et al., 2017). This results in unquantified uncertainties because the AAE and MAC_BCs can vary with specific fuel subtypes and combustion conditions (Wang et al., 2018; Tian et al., 2019). Therefore site-dependent AAEs and MAC_BC are essential for improving the reliability of BC source apportionment by the aethalometer model.

In this study, field measurements of BC were taken on the southeastern margin of the TP during the pre-monsoon. This region connects the high-altitude TP with the low-altitude Yungui Plateau and forms a transport channel for pollutants from Southeast Asia (Wang et al., 2019a), and it is an ideal region for investigating the impact of pollutant transport to the southeastern TP. A receptor model combined with multi-wavelength absorption with aerosol species concentrations was used to retrieve site-dependent AAEs and MAC_BCs. This was done to improve the aethalometer model with the goal of obtaining a more accurate BC source apportionment. The primary objectives of this study were to (1) quantify the mass concentrations of BC from fossil fuel and biomass-burning sources, (2) determine the impact of regional transport on source-specific BC, and (3) assess the radiative effects caused by BC from different sources. This study provides insights into the BC sources on the southeastern TP and an assessment of their radiative effects during the pre-monsoon.

2 Methodology

2.1 Sampling site

Intensive field measurements were made at the rooftop of a building (∼ 10 m above the ground) at the Lijiang Astronomical Station, Chinese Academy of Sciences (3260 m above sea level; $\mathrm{26}{}^{\circ }{\mathrm{41}}^{\prime }{\mathrm{24}}^{\prime \prime }$ N, $\mathrm{100}{}^{\circ }{\mathrm{1}}^{\prime }{\mathrm{48}}^{\prime \prime }$ E), Gaomeigu County, Yunnan Province, China (Fig. 1), from 14 March to 13 May 2018. During the campaign, westerly winds created a potential pathway for cross-border transport from Southeast Asia to southwestern China. During the study, the average relative humidity and temperature were 80 % ± 20 % and 7.6 ± 3.2^∘, respectively; the mean wind speed near the surface was 5.4 ± 2.1 m s⁻¹, and the winds were mainly from the west and southwest. The sampling site is 3–5 km from Gaomeigu village, which has 27 households and 110 residents. Villagers there rely on farming for their livelihoods, and biomass is the primary residential fuel (Li et al., 2016). There are no large industries near the village, and traffic is light. However, two highways (Hangzhou–Ruili Expressway and Dali–Nujiang Expressway) are located ∼ 5.5 km to the west of the sampling site.

Figure 1 Location of the Gaomeigu sampling site (red star) on the southeastern margin of the Tibetan Plateau and prevailing wind during sampling period.

2.2 Online and offline measurements

Aerosol light absorption coefficients at multiple wavelengths (b_abs(λ), λ=370, 470, 520, 590, 660, 880 and 950 nm) were retrieved with the use of a model AE33 aethalometer (Magee Scientific, Berkeley, CA, USA). The sampled particles were selected by a PM_2.5 cuff-off inlet (SCC 1.829, BGI Inc. USA) and dried with a Nafion^® dryer (MD-700-24S-3, Perma Pure, Inc., Lakewood, NJ, USA), and the flow rate of the sampler is 5 L min⁻¹. Detailed operating principles of the AE33 aethalometer can be found in Drinovec et al. (2015). Briefly, light at wavelengths (λ) of 370, 470, 520, 590, 660, 880 and 950 nm emitted from diodes is used to irradiate aerosol deposition spots on the filters. The light attenuation produced by the captured particles is measured with optical detectors. Non-linear loading and filter matrix scattering effects are common issues for filter-based absorption measurements (Collaud Coen et al., 2010). A dual-spot technique for the aethalometer measurements was used to compensate for non-linearity, while a factor of 2.14 was used to correct the artifacts caused by quartz filters (Drinovec et al., 2015).

A photoacoustic extinctiometer (PAX, Droplet Measurement Technology, Boulder, CO, USA) was used to determine the aerosol light scattering and absorption coefficient (b_scat(532) and b_abs(532), respectively), which were used to calculate the single scattering albedo (SSA = $b_{\mathrm{scat}}/(b_{\mathrm{scat}}+b_{\mathrm{abs}}))$ at λ=532 nm. The b_scat was measured using a wide-angle (5–175^∘) integrating reciprocal nephelometer in the scattering chamber. The b_abs(532) was measured simultaneously with an intracavity photoacoustic technique in the acoustic chamber. A detailed description of the PAX can be found in Carrico et al. (2018). During the campaign, the selected concentrations of ammonium sulfate and freshly generated propane soot were used to calibrate the b_scat(532) and b_abs(532) measurements, respectively. Details regarding the calibration procedure are in Wang et al. (2018).

Daily PM_2.5 filters were collected for the analysis of selected chemical species. Organic carbon (OC) and elemental carbon (EC) were determined using a thermal and optical carbon analyzer (Atmoslytic Inc., Calabasas, CA, USA). Water-soluble potassium ions (i.e., K⁺) and levoglucosan were analyzed with the use of an ion chromatograph (Dionex Inc., Sunnyvale, CA, USA). The inorganic elements (i.e., S, Ca, Ti, Mn, Fe, Cu, As, Br, Pb, Zn) were measured using an energy-dispersive X-ray fluorescence spectrometry (Epsilon 5 ED-XRF, PANalytical B.V., Netherlands). Finally, an organic marker of benzothiazolone was determined using a high-performance liquid chromatography (Series 1200, Agilent Technology, Santa Clara, CA). Detailed descriptions of the chemical analyses are given in Text S1 in the Supplement.

2.3 BC source apportionment

The aethalometer model proposed by Sandradewi et al. (2008) was optimized by excluding the b_abs(370) contributed by the secondary aerosols and soil dust (b_abs(370)_secondary and b_abs(370)_dust, respectively).

The formulae used for the aethalometer model were as follows:

$$\begin{array}{}\text{(1)}& {\displaystyle \frac{b_{\mathrm{abs}}(\mathrm{370}{)}_{\mathrm{fossil}}}{b_{\mathrm{abs}}(\mathrm{880}{)}_{\mathrm{fossil}}}}={\left({\displaystyle \frac{\mathrm{370}}{\mathrm{880}}}\right)}^{-{\mathrm{AAE}}_{\mathrm{fossil}}}\text{(2)}& {\displaystyle \frac{b_{\mathrm{abs}}(\mathrm{370}{)}_{\mathrm{biomass}}}{b_{\mathrm{abs}}(\mathrm{880}{)}_{\mathrm{biomass}}}}={\left({\displaystyle \frac{\mathrm{370}}{\mathrm{880}}}\right)}^{-{\mathrm{AAE}}_{\mathrm{biomass}}}\text{(3)}& {\displaystyle }{b}_{\mathrm{abs}}\left(\mathrm{880}\right)=b_{\mathrm{abs}}(\mathrm{880}{)}_{\mathrm{fossil}}+b_{\mathrm{abs}}(\mathrm{880}{)}_{\mathrm{biomass}}\text{(4)}& {\displaystyle }\begin{array}{rl}& b_{\mathrm{abs}}\left(\mathrm{370}\right)=b_{\mathrm{abs}}(\mathrm{370}{)}_{\mathrm{fossil}}+b_{\mathrm{abs}}(\mathrm{370}{)}_{\mathrm{biomass}}\\ & +b_{\mathrm{abs}}(\mathrm{370}{)}_{\mathrm{secondary}}+b_{\mathrm{abs}}(\mathrm{370}{)}_{\mathrm{dust}},\end{array}\end{array}$$

where AAE_fossil and AAE_biomass are the AAEs for emissions of fossil fuel contribution and biomass burning. These were retrieved through an optical source apportionment and are discussed in Sect. 3.1; b_abs(370) and b_abs(880) are the measured b_abs at λ=370 and 880 nm, respectively; the absorption coefficients for fossil fuel are b_abs(370)_fossil and b_abs(880)_fossil, while those for biomass-burning sources are b_abs(370)_biomass and b_abs(880)_biomass. A source apportionment of the optical data was used to calculate b_abs(370)_dust as discussed in Sect. 3.1, while b_abs(370)_secondary was estimated using a BC-tracer method combined with a minimum R-squared approach as described by Wang et al. (2019a).

After obtaining b_abs(880)_fossil and b_abs(880)_biomass, the mass concentrations of BC from fossil fuel combustion and biomass burning (BC_fossil and BC_biomass, respectively) were estimated as follows:

$$\begin{array}{}\text{(5)}& {\displaystyle }{\mathrm{BC}}_{\mathrm{fossil}}={\displaystyle \frac{{\mathrm{b}}_{\mathrm{abs}}(\mathrm{880}{)}_{\mathrm{fossil}}}{{\mathrm{MAC}}_{\mathrm{BC}}(\mathrm{880}{)}_{\mathrm{fossil}}}}\text{(6)}& {\displaystyle }{\mathrm{BC}}_{\mathrm{biomass}}={\displaystyle \frac{{\mathrm{b}}_{\mathrm{abs}}(\mathrm{880}{)}_{\mathrm{biomass}}}{{\mathrm{MAC}}_{\mathrm{BC}}(\mathrm{880}{)}_{\mathrm{biomass}}}},\end{array}$$

where MAC_BC(880)_fossil and MAC_BC(880)_biomass are the MAC_BCs at λ=880 nm generated from fossil fuel combustion and biomass burning, respectively. The MAC_BC(880)s for the two sources were retrieved from a source apportionment of the optical data discussed in Sect. 3.1.

2.4 Optical source apportionment

Optical source apportionments were obtained using a positive matrix factorization (PMF) model. The fundamental objective of the PMF for applications such as ours is to resolve the chemical mass balance by separating the data matrix into factor contributions and factor profiles as follows:

$$\begin{array}{}\text{(7)}& X_{ij}={\sum }_{k=\mathrm{1}}^p{g}_{ik}{f}_{kj}+e_{ij},\end{array}$$

where X_ij represents the input matrix elements, p is the number of sources, g_ik is the source contribution of the kth factor to the ith sample, f_kj is the factor profile of jth species in the kth factor, and e_ij is representative of the residual. The values g_ik and f_kj are non-negative. The two matrices are resolved by minimizing the sum of squares of the normalized residuals as follows:

$$\begin{array}{}\text{(8)}& Q={\sum }_{i=\mathrm{1}}^n{\sum }_{j=\mathrm{0}}^n{\left[{\displaystyle \frac{e_{ij}}{u_{ij}}}\right]}^{\mathrm{2}},\end{array}$$

where Q represents the object function, and u_ij denotes the uncertainties in X_ij. The PMF version 5.0 (PMF5.0, from the US EPA) was used for the analyses, and the optical parameters (primary b_abs at different wavelengths) and chemical species concentrations (including carbonaceous aerosols, inorganic elements, K⁺, levoglucosan and organic markers) were used as model inputs for the optical source apportionment.

2.5 Trajectory-related analysis

To determine the influences of regional transport on BC at Gaomeigu, trajectory clusters were produced from hourly 3 d backward-in-time air mass trajectories at 500 m above the ground level. The trajectories were calculated with the Hybrid Single-Particle Lagrangian Integrated Trajectory model (Draxler and Hess, 1998). The meteorological data were from the Global Data Assimilation System (GDAS; https://www.ready.noaa.gov/gdas1.php, last access: 17 April 2020), which takes into account the influences of terrain. As we focused on differentiating and clustering the main spatial features of the incoming trajectories, an angle-oriented distance definition was adopted in the cluster analysis. Details regarding the trajectory clustering methods can be found in Wang et al. (2018). For the investigations of the effects of transport on the chemical and optical properties of the BC aerosol, trajectories with BC mass concentration greater than the 75th percentile were considered to be polluted.

The potential source contribution function (PSCF) was used to identify the likely pollution regions that influenced BC loadings at Gaomeigu based on the back trajectories. The geographic region covered by the trajectories was overlaid by a 0.5^∘ × 0.5^∘ (i, j) grid. The PSCF value of each grid was calculated as follows:

$$\begin{array}{}\text{(9)}& {\mathrm{PSCF}}_{ij}={\displaystyle \frac{m_{ij}}{n_{ij}}},\end{array}$$

where m_ij is the number of endpoints associated with BC mass concentration higher than the set criterion, and n_ij is the total endpoints of the ijth cell. To improve the resolution of PSCF source identifications, the 75th percentile of each source's BC mass concentration was set as the criterion for a polluted sample (i.e., 0.6 µg m⁻³ for BC_biomass and 0.45 µg m⁻³ for BC_fossil) (Cheng and Lin, 2001). Furthermore, arbitrary weighting factors (w_ij) were applied to different n_ij ranges to reduce the uncertainty caused by the small n_ij (Polissar et al., 1999), and they were defined by the following the approach of Polissar et al. (2001):

$$\begin{array}{}\text{(10)}& W_{ij}=\left\{\begin{array}{l}\mathrm{1}\mathrm{80}<N_{ij}\\ \mathrm{0.7}\mathrm{20}<N_{ij}\le \mathrm{80}\\ \mathrm{0.42}\mathrm{10}<N_{ij}\le \mathrm{20}\\ \mathrm{0.05}{N}_{ij}\le \mathrm{10}.\end{array}\right.\end{array}$$

2.6 Regional chemical dynamical model

The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) was used to quantify the contribution of biomass burning from Southeast Asia to BC mass at Gaomeigu. Detailed descriptions of the model configurations have been described in our previous publication (Xing et al., 2020). Briefly, the model resolution was 3 km × 3 km, and there were 320 grid cells. The domain included the southwest of China and southern and southeastern Asia, with a center at 26.70^∘ N, 100.03^∘ E. Thirty-five vertical layers have been set in the model from the ground surface to 50 hPa. The BC emission inventory used for the model was based on the Asian anthropogenic emission inventory (that is MIX) for the year 2010 (Li et al., 2017). The inventory has a spatial resolution of 0.25^∘ × 0.25^∘, and it included industry, power, transportation and residential sources (e.g., fossil fuel and biofuel). The FINN fire inventory (Wiedinmyer et al., 2011) was used for the biomass-burning emission during the simulation.

2.7 Estimations of direct radiative effects and heating rate

The direct radiative effect (DRE) of source-specific BC were estimated with the widely used Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model, a detailed description of which may be found in Ricchiazzi and Yang (1998). The important input parameters include aerosol optical depth (AOD), light extinction coefficient (b_scat+b_abs), SSA, asymmetric parameter (ASP) and visibility. Here, the input optical parameters were estimated by the Optical Property of Aerosol and Cloud (OPAC) model using Mie theory (Hess et al., 1998). The measured BC, water-soluble matter (including measured water-soluble inorganic ions and water-soluble organic matter that assumes accounting for 79 % of OC loading; Xu et al., 2015) and water-insoluble matter (calculated as the PM_2.5 mass concentration minus that of BC and water-soluble matter) were used in the OPAC model to retrieve the number concentrations of these particles. These were tuned until the modeled b_scat(λ), b_abs(λ) and SSA were within ±5 % (Srivastava et al., 2012) of the values measured by the PAX (see Table S1 in the Supplement) and used to obtain the optical parameters at the nearest observed relative humidity. The underlying assumption was that when the modeled b_scat(λ) and b_abs(λ) were very close to their measured counterparts, the derived optical parameters were a reasonable representation of the measured aerosols. This assumption has been widely used in previous studies (Dumka et al., 2018; Panicker et al., 2010; Rajesh and Ramachandran, 2018). Finally, the DREs attributable to source-specific BC (or PM_2.5) at the surface atmosphere (SUF) and the top of the atmosphere (TOA) were estimated as the difference in the net flux with and without BC (or PM_2.5) under cloud-free conditions. The solar-heating-rate change induced by atmospheric DRE (DRE at TOA subtracts DRE at SUF) was calculated as follows (Ramachandran and Kedia, 2010):

$$\begin{array}{}\text{(11)}& {\displaystyle }{\mathrm{DRE}}_{\mathrm{ATM}}={\mathrm{DRE}}_{\mathrm{TOP}}-{\mathrm{DRE}}_{\mathrm{SUF}}\text{(12)}& {\displaystyle \frac{\partial T}{\partial t}}={\displaystyle \frac{g}{C_p}}\times {\displaystyle \frac{{\mathrm{DRE}}_{\mathrm{ATM}}}{\mathrm{\Delta }P}},\end{array}$$

where DRE_ATM (W m⁻²) is the atmospheric DRE, DRE_TOP is the DRE at top atmosphere, DRE_SUF is the DRE at surface atmosphere, $\frac{\partial T}{\partial t}$ is the heating rate (K d⁻¹), g is the acceleration due to gravity (9.8 m s⁻²), C_p is the specific heat capacity of air at constant pressure, and ΔP is the difference in atmospheric pressure between the ground and 3 km above.

3 Results and discussion

3.1 Source-dependent AAEs and MACs

Four sources were identified as the main contributors to primary b_abs(λ) based on the optical source apportionment (Fig. 2). The simulated primary b_abs(λ) values at different wavelengths all correlated well (r=0.96–0.97, p<0.01; Fig. S1) with the model inputs, indicating that the PMF5.0 performed well. As shown in Fig. 2, the first source factor exhibited high contributions of K⁺ (90 %), levoglucosan (60 %) and primary b_abs(λ) (45 %–64 %) as well as moderate loadings of OC (38 %) and EC (47 %). The K⁺ and levoglucosan are widely used markers for biomass burning (Urban et al., 2012), and these chemical markers are strong indications that this factor resulted from biomass-burning emissions. Furthermore, we note that the presence of BrC in this factor made higher absorption at shorter wavelengths, which is consistent with absorption features of biomass-burning emissions (Forello et al., 2019).

Figure 2 Four aerosol light absorption sources identified by a positive matrix factorization (PMF) model. Concentrations (µg m⁻³) of the chemical species for each source are in purple. Primary b_abs (λ) at six wavelengths (λ=370, 470, 520, 590, 660 or 880 nm) in each source (M m⁻¹) is in green.

Based on the contributions of biomass burning to b_abs(370) and b_abs(880), the AAE_biomass was estimated to be 1.7, which is within a relative boarder range of AAE_biomass (1.2–3.5) determined by other methods (e.g., ¹⁴C and organic tracers) in previous studies (Sandradewi et al., 2008; Helin et al., 2018; Harrison et al., 2012; Zotter et al., 2017). The estimated average MAC_BC(880)_biomass was 10.4 m² g⁻¹; this is more than twice the value for uncoated BC particles suggested by Bond and Bergstrom (2006) (MAC_BC(880)_uncoated=4.7 m² g⁻¹, extrapolated from 550 to 880 nm by assuming AAE_BC = 1.0). The large MAC_BC(880) indicates that BC particles from biomass burning experienced substantial aging processes during their transport because numerous studies have confirmed that aged BC could result in MAC increases by a factor of 1.5–3.5 relative to uncoated particles (Chen et al., 2017; Ma et al., 2020) due to the “lensing effect” (Lack and Cappa, 2010).

The second source factor was characterized by large loadings of benzothiazolone (54 %), Pb (46 %), Br (40 %), Cu (35 %), Zn (27 %), EC (36 %) and OC (30 %). Benzothiazolone is released from the breakdown of the antioxidant in motor vehicle tires (Cheng et al., 2006), while Br is another tracer of motor vehicle emission (Guo et al., 2009). Similarly, Zn and Cu are associated with the combustion of lubricating fluids and the wear of brakes and tires (Lough et al., 2005; Song et al., 2006). Finally, EC and OC also are components of motor vehicle emissions (Cao et al., 2013). Although unleaded gasoline has been used extensively in China since 2005, a considerable portion of Pb in the environment is still associated with vehicle-related particles, especially from the wear of metal alloys (Hao et al., 2019). Therefore, this second source factor was identified as traffic-related emissions. This source constitutes a moderate percentage of primary b_abs(λ) (15 %–30 %). The estimated traffic-emission-related AAE (AAE_traffic) was 0.8, consistent with the finding that BC is the dominant light-absorbing carbonaceous aerosol species for traffic emissions (Kirchstetter et al., 2004). The AAE_traffic found here also was close to a value obtained using the ¹⁴C approach (Zotter et al., 2017). The estimated MAC_BC(880) of traffic emissions (MAC_BC(880)_traffic=9.1 m² g⁻¹) was similar with MAC_BC(880)_biomass, indicating that traffic-emission-related BC particles were also subjected to substantial aging.

The third factor was dominated by high loadings of As (70 %), S (37 %) and Cu (47 %), which are typically associated with coal combustion (Hsu et al., 2016; Kim and Hopke, 2008). Although coal is not used extensively near the site on the TP, emissions from coal combustion may have been transported to the site from surrounding areas (e.g., East Asia; Li et al., 2016). This source contributed 12 %–17 % of primary b_abs(λ), which is less than that from biomass burning or traffic emissions. The obtained AAE of coal combustion (AAE_coal=1.1) was similar to the AAE_traffic, suggesting that BC was also the dominant light-absorbing carbon species in coal combustion emissions. The AAE_coal was close to the value of chunk coal combustion (1.3) but lower than that for briquettes of coal (2.6) (Sun et al., 2017); this presumably reflects the types of coal transported to Gaomeigu, at least to some degree. The estimated BC MAC(880) of coal combustion (MAC_BC(880)_coal=15.5 m² g⁻¹) was larger than MAC_BC(880)_biomass and MAC_BC(880)_traffic. The enhancement factor for MAC_BC(880)_coal (3.3) falls near the upper limit of this range noted above, and although this is likely related to the aging of BC particles during transport to Gaomeigu, more work is need to verify this contention in future studies.

The fourth source factor had high loadings of Ca (35 %), Ti (66 %), Mn (47 %) and Fe (61 %), consistent with the characteristics of crustal elements (Guo et al., 2009); thus, it was assigned to soil dust. The light absorption of soil dust is mainly due to the presence of iron oxides and varies with the types and relative concentrations of iron oxide species (Alfaro, 2004; Valenzuela et al., 2015). The mineral dust here contributed a relatively small amount to the primary b_abs(λ) (6 %–9 %), presumably due to the low levels of iron oxides, and this is consistent with the other results obtained on the southeastern TP (Zhao et al., 2019). The estimated AAE of mineral dust (AAE_dust) was 1.5, which is within the range of 1.2–3.0 obtained from multiple non-oceanic sites (Dubovik et al., 2002).

3.2 BC source apportionment

As the results above show that fossil-fuel-related BC aerosol mainly originated from traffic and coal combustion, the AAE_fossil (0.9) and MAC_BC(880)_fossil (12.3 m² g⁻¹) were averaged by the values of AAE_traffic + AAE_coal and MAC_BC(880)_traffic + MAC_BC(880)_coal, respectively, as shown in Table 1. Based on the source-specific AAEs (i.e., AAE_fossil and AAE_biomass) and MAC_BC(880) (i.e., MAC_BC(880)_fossil and MAC_BC(880)_biomass), the mass concentrations of BC_fossil and BC_biomass were then estimated using the improved aethalometer model (Eqs. 1–6). As shown in Fig. 3a, no correlation (r=0.01, p=0.02) was found between BC_biomass and BC_fossil, implying that BC from these two sources was effectively separated by the improved aethalometer model. The data for the biomass-burning and traffic-related tracers (levoglucosan and benzothiazolone, respectively) further support the results of BC source apportionment. That is, BC_biomass was significantly correlated with levoglucosan (r=0.75, p<0.01; Fig. 3b), and the same was true for BC_fossil benzothiazolone (r=0.67, p<0.01; Fig. 3c), respectively. These results indicate that the source-specific AAEs and MACs(880) obtained from optical source apportionment were appropriate

Table 1 Derived Ångström absorption exponents (AAEs), mass absorption coefficients (MACs) and percent source contribution of black carbon (BC) from difference sources.

Figure 3 Scatterplots of (a) biomass-burning black carbon (BC_biomass) versus fossil fuel combustion BC (BC_fossil), (b) BC_biomass versus levoglucosan and (c) BC_fossil versus benzothiazolone. (d) Time series of hourly averaged mass concentrations of BC_biomass and BC_fossil.

Figure 3d shows a time series plot of hourly averaged mass concentrations of total BC, BC_biomass and BC_fossil during the campaign. The hourly total BC mass concentration varied ∼ 50-fold, from 0.1 to 4.9 µg m⁻³, with an arithmetic mean (± standard deviation) of 0.7 (±0.5) µg m⁻³, which was lower than what has been reported for the western TP but higher than on the northern TP (Wang et al., 2018, and references therein). The larger BC loading on the western TP can be explained by relatively strong influences from Southeast Asia, where anthropogenic activities are intensive (Kurokawa et al., 2013). With reference to BC sources, the concentrations and contributions as listed in Table 1 show higher mass concentrations of BC_biomass (0.4 ± 0.3 µg m⁻³, 57 % of total BC) compared with BC_fossil (0.3 ±  0.2 µg m⁻³, 43 % of total BC) on average. The mass fraction of BC_biomass increased with the BC loadings, while BC_fossil mass fraction showed an inverse relationship to the loadings (Fig. S2). One can infer from this that biomass-burning emissions were responsible for the high-BC-loading episode during the campaign.

Distinct diurnal variations in the mass concentrations of BC_biomass and BC_fossil were observed as shown in Fig. 4a. The BC_biomass started to increase after midnight, reached a small peak at ∼ 05:00 (all times given in local time: UTC+8) and then remained at a constant level before sunrise (∼ 08:00). This may be attributed to effects associated with changes in the height of the planetary boundary layer (PBL) (https://rda.ucar.edu/datasets/ds083.2, last access: 17 April 2020) (Fig. 4b). Thereafter, the BC_biomass increased again and reached the maximum value at midday. This enhancement was accompanied by an increase in PBL height and higher wind speed (https://rda.ucar.edu/datasets/ds083.2) (Fig. 4b). Generally, higher PBLs and stronger wind cause local pollutants to disperse and as a result lower their loadings (Wang et al., 2015). However, the buildup of BC_biomass in daytime at Gaomeigu was more likely influenced by the transport of BC_biomass from regions upwind. After sunrise, the PBL began to deepen, and that was accompanied with west and southwest winds from 08:00 to 12:00 (Fig. S3). These meteorological conditions are favorable for pollutant (including BC) transport from high-density biomass-burning emission areas to the sampling site (Chan et al., 2017). After the midday peak, BC_biomass decreased sharply until midnight. The initial portion of this decrease (13:00–18:00) occurred as the PBL height and wind speed increased, which promoted the dispersion of BC_biomass. Subsequent reduction occurred at night even though the PBL height and wind speed decreased, and that was likely due to the curtailment of local biomass-burning activities.

Figure 4 Left panels: diurnal variations in hourly averaged black carbon (BC) aerosol from biomass burning (BC_biomass) and fossil fuel sources (BC_fossil). Right panels: wind speeds and planetary boundary layer (PBL) heights during the entire campaign and for different air mass directions determined by air mass trajectory clusters 1–3.

As shown in Fig. 4a, the BC_fossil showed a diurnal trend that was roughly opposite of that of the unimodal pattern seen for BC_biomass, and that may be explained as follows. Increases in the PBL height and wind speed from 09:00 to 15:00 were associated with a decrease in BC_fossil, unlike the increasing trend seen for BC_biomass; this presumably reflects minor effects from the regional transport on BC_fossil. Further, because of the small contribution of coal combustion to EC (12 %; Fig. 2c), the BC_fossil was best explained by motor vehicle emissions from areas upwind of the site. The subsequent increase in BC_fossil from 17:00 to 20:00 was attributed to the reduction in PBL height and as a result the buildup of pollutants in the near-surface air. As there were minimal impacts from traffic at night, the BC_fossil loadings remained steady from 21:00 to 08:00. The stable nocturnal BC_fossil may reflect the impact of fossil fuel emissions on BC in the southeastern margin of the TP due to the accumulation resulting from the low PBL heights.

3.3 Regional influences of BC_biomass and BC_fossil

To investigate the regional impacts on BC, three groups of air masses were identified based on their transport pathways (Fig. 5a). Cluster 1 originated from northeastern India and then passed over Bangladesh before arriving at Gaomeigu. The average BC mass concentration of this cluster was the highest (0.8 ± 0.4 µg m⁻³) of the three clusters. About 74 % of total trajectories were associated with Cluster 1, of which 22 % were identified as polluted and had an average BC loading of 1.3 ± 0.5 µg m⁻³. Cluster 2 originated over Burma and had an average BC loading of 0.7 ± 0.7 µg m⁻³. This cluster accounted for only 24 % of total trajectories, but among them, about 37 % referred to pollution with BC reaching as high as 1.6 ± 0.9 µg m⁻³. The air masses associated with Cluster 3 originated from the interior of China, and this group had the lowest BC mass concentrations of the three clusters: 0.4 ± 0.1 µg m⁻³. This third cluster was composed of a small fraction of total trajectories (2 %), and none of them were identified as polluted, suggesting minor influences from mainland China during the campaign.

Figure 5 Maps of (a) the mean trajectory clusters, (b) the potential source contribution function for black carbon (BC) aerosol from biomass burning (BC_biomass) and (c) from fossil fuel sources (BC_fossil).

The diurnal patterns of BC_biomass and BC_fossil mass loadings from the three clusters were used to investigate the impacts of regional transport. As shown in Fig. 4c and e, similar diurnal variations in BC_biomass were found for Clusters 1 and 2; they both had larger values during daytime (8:00–12:00) compared with night. This pattern of higher daytime BC_biomass was associated with regional transport from northeastern India (Cluster 1) and Burma (Cluster 2). For Cluster 3, BC_biomass decreased during the day and increased at night (Fig. 4g), and that pattern tracked the daily variations in PBL height. Unlike Clusters 1 and 2, the diurnal variation in BC_biomass for Cluster 3 was more likely due to influences of biomass-burning activities from areas surrounding the sampling site than regional transport. However, it should be noted that these cases were uncommon because only 2 % of air masses were associated with Cluster 3.

For BC_fossil, similar diurnal patterns were found for Clusters 1 and 2 (Fig. 4c and e), most likely due to the influences of traffic emissions from surrounding areas as well as daily cycles of PBL height as discussed in Sect. 3.2. The BC_fossil loadings of Cluster 3 (Fig. 4g) were relatively stable, showing only sporadic fluctuations. Unlike the declining trend of BC_fossil during the daytime found for Clusters 1 and 2, the relatively stable BC_fossil loadings in Cluster 3 indicate that there were emissions from fossil fuel sources that offset the effect of the changes in PBL height. The transportation sector has grown rapidly in mainland China (Liu, 2019), and the regional transport of motor vehicle emissions may have been the cause for the observed diurnal variations in BC_fossil for Cluster 3.

The PSCF model was applied to further investigate the likely spatial distribution of pollution source regions for BC_biomass and BC_fossil. As shown in Fig. 5b, a low PSCF value of BC_biomass was found near Gaomeigu, while high values were concentrated in northeastern India and northern Burma, consistent with intensive fire activities in these areas (Fig. S4). This indicates that large BC_biomass loadings at Gaomeigu were more likely influenced by cross-border transport of biomass burning rather than local emissions. For BC_fossil (Fig. 5c), the most likely impact region was located to the southwest of Gaomeigu, near where two highways are located (e.g., Hangzhou–Ruili Expressway and Dali–Nujiang Expressway). Owing to the low consumption of coal on the southeastern TP (Li et al., 2016), the high PSCF values of BC_fossil were more likely from traffic emissions than coal combustion. Moreover, sporadic high PSCF values of BC_fossil were also found in northern Burma, indicating possible influences of fossil fuel emissions here.

To further quantify the contributions of the BC transported from Southeast Asia to Gaomeigu, we studied a high-BC episode (23–27 March 2018) using a simulation with the WRF-Chem model. Two scenarios of emissions were simulated: one involved all BC emission sources, and the other turned off biomass-burning emissions in Southeast Asia. The variation in modeled BC mass concentration shows an acceptable degree of consistency with the measured values (r=0.63, p<0.01; Fig. S5), Furthermore, the index of agreement was estimated to be 0.77, indicating that the development of this BC episode was effectively captured by the WRF-Chem model. Nonetheless, the normalized mean bias between the measured and modeled BC values was estimated to be 24 %, suggesting that simulation was biased towards high values. This discrepancy is best attributed to the uncertainties in the simulation associated with the emission inventory and meteorological conditions. Figure 6a shows the spatial distributions of BC loadings in Gaomeigu and surrounding areas. The mass concentrations of BC at times exceeded 15 µg m⁻³ over Burma and northern India, and that is more than an order of magnitude higher compared with the southeastern margin of the TP (0.7 µg m⁻³). After turning off the biomass-burning emission source in Southeast Asia, the BC loading at the southeastern TP dropped over 40 % (Fig. 6b), suggesting a substantial impact of biomass-burning activities in Southeast Asian countries, which is consistent with results of the trajectory cluster analysis and PSCF.

Figure 6 (a) Spatial distributions of simulated aerosol black carbon (BC) mass concentrations over Southeast Asia and (b) the percentage of mass contributions of biomass-burning BC (BC_biomass) in peninsular Southeast Asia (PSEA). The simulated surface winds are overlaid in (a).

3.4 Radiative effects and heating rate

Figure 7 shows the average atmospheric direct radiative effects of PM_2.5 and BC (including BC_biomass and BC_fossil) at the TOA and SUF during the campaign. The average PM_2.5 DRE at the TOA was +0.03 ± 1.1 W m⁻², implying that the positive effect of light-absorbing carbon on the temperature of the atmosphere outweighed the negative effect of scattering aerosols. In fact, BC produced +1.6 ± 0.8 W m⁻² at the TOA on average. At the SUF, BC DRE (−3.0 ± 1.5 W m⁻²) contributed nearly half of the PM_2.5 DRE (−6.3 ± 4.5 W m⁻²). The difference in the DRE between the TOA and SUF was +4.6 ± 2.4 W m⁻² for BC aerosol, and that accounted for 73 % of the DRE attributed to PM_2.5 (6.3 ± 4 W m⁻²); this suggests a substantial radiative effect caused by BC over the southeastern margin of the TP even though its mass fraction is small in PM_2.5 (3.3 %).

Figure 7 The average atmospheric direct radiative effects (DREs) of total black carbon (BC); BC from biomass burning (BC_biomass) and fossil fuel (BC_fossil); and PM_2.5 at the Earth's surface (SUF), the top of the atmosphere (TOA) and in the atmosphere (ATM = TOA − SUF).

With respect to the BC sources (Fig. 7), the average BC_biomass (BC_fossil) DRE was +0.8 ± 0.6 W m⁻² (+0.7 ± 0.4 W m⁻²) at the TOA and −1.7 ± 1.2 W m⁻² (−1.4 ± 0.6 W m⁻²) at the SUF. This is equivalent to an average atmospheric DRE of +2.5 ± 1.8 W m⁻² (+2.1 ± 0.9 W m⁻²). Presumably, the influences of regional transport caused the atmospheric DRE of BC_biomass to be more variable compared with that from BC_fossil (Fig. S6). For example, the atmospheric DRE of BC_biomass can be as high as +6.4 W m⁻² when the air masses passed over the biomass-burning regions in Southeast Asia, while it was only 1.1 ± 0.2 W m⁻² on average when the air masses passed from the mainland of China.

The calculations showing a positive atmospheric DRE imply that energy was trapped in the atmosphere, which would lead to atmospheric heating over the study region. The heating rate calculated for BC varied from 0.02 to 0.3 K d⁻¹ and yielded an average of 0.13 ± 0.07 K d⁻¹. In terms of the DRE efficiency, the heating rate caused by a unit mass concentration of BC in this region was (0.19 (K d⁻¹) (µg m⁻³)⁻¹), which is roughly comparable with that reported for Qinghai Lake, on the northeastern TP (0.13 (K d⁻¹) (µg m⁻³)⁻¹) (Wang et al., 2015), but it is generally lower than the values in the southwestern regions of the Himalaya (Fig. S7). Moreover, the heating rate caused by BC_biomass may have been slightly higher (0.07 ± 0.05 K d⁻¹) compared with BC_fossil (0.06 ± 0.02 K d⁻¹). Finally, the heating rate of BC_biomass increased to 0.16 K d⁻¹ when the BC mass concentration was heavily influenced by the polluted air from Southeast Asia.

4 Conclusions

This study quantified the source contributions of BC aerosol from fossil fuel and biomass burning at a site on the southeastern margin of the TP that represents a regional transport channel for air pollution during the pre-monsoon. The study was conducted in the pre-monsoon when the southeastern TP was heavily influenced by the air mass from Southeast Asia. To reduce the uncertainties caused by interferences in absorption measurements (i.e. secondary absorption and dust) and assumptions relative to AAEs and MAC_BCs, the traditional aethalometer model was optimized in two aspects. First, a BC-tracer method coupled with a minimum R-squared approach was applied to separate secondary absorption from the total absorption, and as a result, the interferences of absorption from secondary aerosols have been eliminated. Then, an optical-source-apportionment model that used primary multi-wavelength absorption and chemical species as inputs was used to derive site-dependent AAE and MAC_BC values; these minimize the uncertainties associated with prior assumptions on these parameters. The AAE (MAC_BC) calculated in this way was 0.9 (12.3 m² g⁻¹) for fossil fuel source and 1.7 (10.4 m² g⁻¹) for biomass burning. The results of aethalometer model that used these values showed that the average mass concentration of BC was 0.7 ± 0.5 µg m⁻³, of which 43 % was BC from fossil fuel, and 57 % was from biomass burning. Trajectory analysis showed that the BC_biomass over the site was mainly driven by regional transport from northeastern India and Burma, while BC_fossil was primarily influenced by traffic emissions from areas surrounding the sampling site. Moreover, the WRF-Chem model indicates that biomass burning in Southeast Asia contributes 40 % of the BC loading over the southeastern margin of the TP. The SBDART model showed a DRE of +4.6 ± 2.4 W m⁻² for the total PM_2.5 BC, of which +2.5 ± 1.8 W m⁻² was from BC_biomass and +2.1 ± 0.9 W m⁻² from BC_fossil. The results of this study provide useful information concerning the sources of BC over an atmospheric transport channel to the southeastern TP, and they highlight the importance of the cross-border transport of biomass-burning emissions from Southeast Asia to the region during the pre-monsoon.