Anthropogenic iron oxide aerosols enhance atmospheric heating

Abstract

Combustion-induced carbonaceous aerosols, particularly black carbon (BC) and brown carbon (BrC), have been largely considered as the only significant anthropogenic contributors to shortwave atmospheric heating. Natural iron oxide (FeO_x) has been recognized as an important contributor, but the potential contribution of anthropogenic FeO_x is unknown. In this study, we quantify the abundance of FeO_x over East Asia through aircraft measurements using a modified single-particle soot photometer. The majority of airborne FeO_x particles in the continental outflows are of anthropogenic origin in the form of aggregated magnetite nanoparticles. The shortwave absorbing powers (P_abs) attributable to FeO_x and to BC are calculated on the basis of their size-resolved mass concentrations and the mean P_abs(FeO_x)/P_abs(BC) ratio in the continental outflows is estimated to be at least 4–7%. We demonstrate that in addition to carbonaceous aerosols the aggregate of magnetite nanoparticles is a significant anthropogenic contributor to shortwave atmospheric heating.

Introduction

Dark-coloured aerosols such as combustion-induced carbonaceous particles and wind-blown mineral dust absorb solar radiation and perturb the climate system by heating the atmosphere and reducing the snow albedo^1,2. Shortwave atmospheric heating by such aerosols can alter the cloud fraction and hydrological cycle on both regional and global scales^3,4,5,6. The global mean increase in precipitation per degree of increase in global-mean surface temperature, attributable to human-induced global warming, strongly depends on the emission of black carbon (BC), a proxy for anthropogenic shortwave absorbers⁶. To evaluate the effects of dark-coloured aerosols on climate using numerical models, observational datasets are needed to constrain the sources, atmospheric abundance, and detailed microphysical properties (for example, size distribution and morphology) of individual light-absorbing aerosols^1,7. The long-term data on absorption aerosol optical depth measured by ground-based remote sensing⁸ have been the main observational constraints for numerical models used to evaluate the effects of absorbing aerosols on climate change^1,7. Inverting the remote sensing data to the column abundance of absorbing aerosols requires a priori assumption on the optical properties of each type of aerosols that contributes to absorption aerosol optical depth⁹. Therefore, in situ observations of all the important contributors to atmospheric shortwave absorption form the basis for the quantitative investigation of the complex effects of absorbing aerosols on climate.

Until now, two types of carbonaceous aerosols, BC and brown carbon (BrC), and wind-blown mineral dust (DU) have generally been considered as the only three aerosol species that contribute significantly to shortwave absorption in the atmosphere and snowpack^1,2,8,10,11. BC and BrC are mostly co-emitted during the burning of fossil fuels, biofuels and biomass⁷. Light absorption by DU is mostly due to iron oxide nanoparticles attached on the surfaces of host mineral materials¹². DU absorption dominates the atmospheric shortwave absorption over the desert and dust outflow regions¹³. A number of attempts have recently been made to quantify BrC absorption separately from BC absorption^11,14,15,16. These studies suggest that BC absorption almost invariably dominates BrC absorption, except at ultraviolet wavelengths. In Beijing, an indirect estimate using ground-based remote-sensing technique¹⁴ indicated that BrC absorption is ∼10% of BC absorption at the mid-visible wavelength in the winter and spring seasons. Another estimate of BrC absorption in Beijing using in situ optical and chemical measurements in March provided a similar result¹¹. Using a global chemical transport model, Feng et al. estimated that BrC accounts for 7–19% of aerosol absorption (global average)¹⁷.

Recent observational studies using electron microscopy methods revealed that anthropogenic iron oxide particles in the form of aggregated FeO_x nanoparticles are ubiquitous in urban atmospheres^18,19,20 and roadside environments²¹. They are emitted from, for example, the blast furnaces of iron manufacturing facilities²² and from the engines and brakes of motor vehicles^23,24. The major iron oxide phases of aggregated FeO_x nanoparticles are magnetite (Fe₃O₄)^{18,20,21,23,24}, maghemite (γ-Fe₂O₃)^21,24 and hematite (α-Fe₂O₃)^18,21,24. However, their abundances in the troposphere and radiative effects have not yet been evaluated.

In this study, in situ aircraft measurements using a modified single-particle soot photometer (SP2)²⁵ and electron microscopy are performed to show that anthropogenic FeO_x particles, particularly aggregated magnetite nanoparticles, are ubiquitous in the continental outflows from East Asia. We then evaluate their contribution to atmospheric shortwave absorption on the basis of the observed size-resolved mass concentrations and particle morphologies. Our results indicate that the absorption by anthropogenic FeO_x is at least 4–7% of the BC absorption over East Asia.

Results

Observation

We used observational data from the Aerosol Radiative Forcing in East Asia (A-FORCE) 2013W aircraft campaign²⁶ over Yellow Sea and East China Sea in February and March, 2013. Our modified SP2 can measure individual BC and FeO_x particles in the mass-equivalent diameter (D_m) domains of 70 nm≤D_m≤850 nm and 170 nm≤D_m≤2,100 nm, respectively²⁵. The aerosol-sampling system, which consisted of a forward-facing shrouded solid diffuser inlet, transport tubes and aerosol measuring instruments, was designed to observe submicron-sized particles. The theoretical transmission efficiency curves Tr(D_m) of FeO_x particles began to drop at D_m=∼600 nm and decreased to ∼0.5 at D_m=2,100 nm (Supplementary Fig. 1). These theoretical Tr(D_m) curves suggest that our measurement system underestimates the ambient FeO_x concentrations at D_m>∼600 nm.

Characterization of anthropogenic FeO_x

In this section, we characterize the fundamental properties of airborne FeO_x particles in the East Asian continental outflows. For this purpose, we focus on the air parcel passed through the planetary boundary layer over eastern China and was directly transported to the sampling point on the flight track below 2 km altitude without experiencing wet removal of aerosols. The aircraft observation data of this air parcel is called dry PBL air²⁶. The detailed criteria for selecting the dry PBL air according to the backward trajectory analysis is described in Kondo et al.²⁶.

First, we classified individual Fe-bearing particles in dry PBL air depending on the composition and morphology, based on the electron-microscopy analyses of 1,460 particles collected by an onboard aerosol-impactor sampler. A transmission electron microscope (TEM) and a scanning transmission electron microscope equipped with energy-dispersive X-ray spectrometry (STEM–EDS)¹⁹ were used for these analyses. Table 1 lists the number of Fe-bearing particles for each of the three morphological types measured by the STEM–EDS analyses. The most abundant type of Fe-bearing particle was aggregated FeO_x nanoparticles; the TEM images and associated elemental mappings of these nanoparticles are shown in Fig. 1; Supplementary Fig. 2. The diameters of the FeO_x monomers comprising each aggregate were highly variable, ranging from several nm to ∼100 nm. These composition and morphology of FeO_x particles are similar to those found in Tokyo¹⁹ or Mexico City²⁷. Our electron energy loss spectroscopy (EELS) analyses with TEM showed that the major component of the aggregated FeO_x nanoparticles in dry PBL air is magnetite (Supplementary Fig. 3). The second most abundant type of Fe-bearing particles in dry PBL air was fly ash, which is a complex internal mixture of combustion-induced refractory materials commonly includes Si as a major component. The Fe-bearing fly ash particles in the dry PBL air typically contained only several per cent of Fe by mass. A third type of Fe-bearing particle, Fe-bearing mineral dust, was rare in the dry PBL air; the fraction of mineral dust particles among all the Fe-bearing particles was only 0.035 (Table 1). Although the mineral dust was observed to be minor in the dry PBL air, it would be the dominant type of Fe-bearing particles over the East Asia during an Aeolian dust (that is, Kosa) outflow event.

Table 1 Number of particles determined by STEM–EDS analysis for samples collected in dry PBL air.

Figure 1: Transmission electron microscopy images and element distributions of aggregated iron oxide nanoparticles found in dry PBL air.

The sample was collected using an aerosol-impactor sampler onboard the aircraft from 14:35 to 14:47 on 4 March 2013 (local time) during the A-FORCE 2013W campaign (that is, Sample number 1 in Table 1). The particles magnified in a and b were collected on the same substrate with an inter-particle distance of ∼4 μm, as shown in c. The elemental distributions of Fe and O for particles a and b are shown in d and e, respectively, and indicate that these particles contain iron oxide.

Next, we present results provided by the light-scattering signals of individual FeO_x particles measured by the SP2. Figure 2 shows a scatterplot of the scattering cross-section at the onset of the incandescence (C_s-oi)²⁸ and the mass-equivalent diameter D_m for both laboratory and ambient FeO_x-containing particles. Figure 2 also includes experimental results for pure magnetite particles and mineral dust particles (Icelandic dust and Taklamakan dust)²⁵. Compared with pure magnetite, the mineral dust samples exhibited large C_s-oi values beyond the detectable limit of ∼4 × 10⁻¹⁴ m², because the host minerals internally mixed with the incandescing FeO_x also contribute to the C_s-oi value. On the basis of these results, we introduced a criterion for classifying the mixing state of detected FeO_x-containing particles in the D_m domain of 170 nm≤D_m≤270 nm. Particles with C_s-oi>2 × 10⁻¹⁴ m² were classified as dust-like. Figure 2 shows that a majority of the FeO_x-containing particles in the dry PBL air were similar to the pure magnetite particles in terms of their C_s-oi value, whereas a minority had substantially larger C_s-oi values. In the dry PBL air, the fraction of dust-like FeO_x particles among the detected FeO_x-containing particles was ∼0.1, as discussed later.

Figure 2: Single-particle soot photometer data indicating the mixing state of individual iron oxide-containing particles.

The figure shows scatterplots of scattering cross-section at the onset of incandescence (C_s-oi) and mass-equivalent diameter D_m for laboratory samples (magnetite, Icelandic dust and Taklamakan dust) and FeO_x-containing particles in dry PBL air. For dry PBL data, the solid red line and two dashed red lines represent median and (10 and 90) percentile C_s-oi values as functions of D_m, respectively. The D_m domain used for classifying the mixing state of FeO_x-containing particles is schematically shown. In this study, ambient FeO_x-containing particles with 170 nm≤D_m≤270 nm are classified as dust-like FeO_x if C_s-oi > 2 × 10⁻¹⁴ m². The detectable upper limit of C_s-oi is ∼4 × 10⁻¹⁴ m².

In addition to the direct microscopic observations using the TEM–EELS, the optical signals measured using the SP2 also provided indirect information on the material properties of the airborne FeO_x. The timing of the onset of incandescence (t_oi) of FeO_x-containing particles has been experimentally shown to be a qualitative indicator of colour darkness (that is, light-absorbing efficiency) in FeO_x materials²⁵. The distribution of the t_oi values of the FeO_x-containing particles in dry PBL air was similar to that for black-coloured magnetite but dissimilar to that of red-coloured hematite (see the Methods section and Supplementary Fig. 4). This implies that the major component of incandescing FeO_x particles in dry PBL air is magnetite, which is consistent with the TEM–EELS results (Supplementary Fig. 3).

We found that the FeO_x number concentration in the dry PBL air was highly correlated with the CO mixing ratio (r²=0.87) and BC number concentration (r²=0.69; Fig. 3a,b, respectively). These results suggest that the spatial distribution of the FeO_x emission flux over the East Asian continent is similar to those of CO and BC. On the other hand, the FeO_x/BC number concentration ratio in the dry PBL air-correlated neither with the relative abundance of the supermicron-sized aerosols (Fig. 3c) nor the number fraction of the dust-like FeO_x-containing particles (Fig. 3d). The scatterplots imply that the majority of the detected FeO_x particles in the dry PBL air are not associated with DU, which is consistent with the results of the direct STEM–EDS analyses of the aerosol-impactor samples (Table 1).

Figure 3: Scatterplots between the 1-min data of various observed parameters in dry PBL air.

Number of data points (n) and the square of the correlation coefficient (r²) are shown in each panel. (a,b) The correlations between the FeO_x concentrations and the concentrations of two representative pollutants in urban air: BC and CO. Particle concentrations were calculated at standard temperature and pressure (STP). The red line represents the linear regression line. (c,d) Scatterplots between the FeO_x/BC concentration ratio and two available indicators of relative abundance of mineral dust particles: (c) number concentration ratio of supermicron-sized aerosols to submicron-sized aerosols and (d) number fraction of dust-like FeO_x particles. In c, N_p denotes the number concentration of aerosols within a particular range of light-scattering equivalent diameter D_p, which was observed using a cloud and aerosol spectrometer probe.

The two most important conclusions drawn from these observations are as follows. First, the FeO_x particles detected using the modified SP2, with the exception of the dust-like FeO_x, are primarily aggregated magnetite nanoparticles of anthropogenic origin. Second, the anthropogenic magnetite is a major type of Fe-bearing aerosol in East Asian continental outflow.

Altitude profiles

Figure 4 shows the size-resolved number and mass concentrations of FeO_x at each altitude along with those in the dry PBL air. Within the observed size domain of 170 nm≤D_m≤2,100 nm, the size-resolved number concentration of FeO_x was approximated using a power function with an offset

Figure 4: Size-resolved number and mass concentrations of iron oxide particles.

Results in dry PBL air and at different altitudes are shown in a and b–f, respectively. Filled circle and error bar represent the sample mean and the sample s.d. (±1σ) of the 1-min data, respectively. The number of 1-min data points (n) is shown in each panel. Dashed lines show the results of least-square fitting of equation (1).

where y₀, a and p are numerical parameters listed in Table 2. The size-resolved mass concentration of FeO_x was approximated as

Table 2 List of the parameters for the size-distribution functions (equations (1) and (3)).

where is the assumed bulk density of FeO_x (5.17 g cm⁻³). In contrast, the size-resolved mass concentration of BC within the observed size domain of 70 nm≤D_m≤850 nm (see Figs 6 and 14 of Kondo et al.²⁶) was approximated with a two-modal lognormal function as

where A_i, D_i and σ_gi (i=0,1) are numerical parameters listed in Table 2. Figure 4 shows that the mode D_m values of the number size distribution were smaller than 170 nm, whereas the mode D_m values of the mass size distribution may have been larger than 2,100 nm, that is, the detectable D_m domain of 170 nm≤D_m≤2,100 nm was insufficient to reveal the entire shape of the size-distribution function of ambient FeO_x. As mentioned earlier, our aerosol-sampling system likely underestimated the ambient FeO_x concentrations for D_m>∼600 nm. In contrast, our BC measurements in the domain of 70 nm≤D_m≤850 nm were likely sufficient to determine the total BC mass concentrations, as expected from the Figure 6 and 14 of Kondo et al.²⁶ and the parameters listed in Table 2.

The BC size distributions tended to shift towards smaller sizes at higher altitudes²⁶ (see parameter D₀ in Table 2). This trend has been explained by the preferential wet removal of larger BC particles during vertical transport^26,29. In contrast, the FeO_x size distributions shifted towards larger sizes with increasing altitude (Fig. 4). The decrease of the parameter p with altitude reflects this trend (Table 2). As shown later, the number fraction of dust-like FeO_x was observed to increase with altitude. We hypothesize that the mineral dust particles lifted from the deserts in central China and transported by the westerlies cause the larger shift of the FeO_x size distributions over the Yellow Sea and East China Sea.

Figure 5a,b show the vertical profiles of the FeO_x concentrations and the FeO_x/BC concentration ratios, respectively. The mean FeO_x mass concentration was 100–400 ng m⁻³ at standard temperature and pressure (STP) in the boundary layer (altitude<∼2 km), and monotonically decreased with altitude to 30–90 ng m⁻³ STP in the free troposphere (altitude>∼3 km). The mean FeO_x/BC number concentration ratio was 0.004 in the boundary layer and decreased to 0.002 in the free troposphere. The mean FeO_x/BC mass concentration ratio was 0.3–0.5 in all altitudes below 6 km, and increased to ∼0.8 at higher altitudes (6–8 km). The observed FeO_x/BC number (mass) ratio would be substantially larger if we were able to detect smaller (larger) FeO_x particles with D_m<170 nm (D_m>2,100 nm). The number fraction of dust-like FeO_x showed a monotonic increase with altitude from ∼0.07 at 0–1 km to ∼0.5 at 6–8 km (Fig. 5c). In an aerosol-impactor sample collected during a time period of the highest FeO_x number concentration observed at 6–8 km altitude (∼0.4 cm⁻³ STP), we actually found mineral dust particles including Fe (Supplementary Fig. 5). However, in the free tropospheric samples, the number of Fe-bearing particles collected on each TEM grid was too low to evaluate the relative abundance of each of the three morphological types listed in Table 1.

Figure 5: Altitude profiles of various observed parameters.

In each panel, the filled circles represent the mean values at each altitude, whereas the filled triangle represents the mean value in dry PBL air. (a) FeO_x mass and number concentrations. The error bars in a represent the sample standard deviation (±1σ) of 1-min data. (b) The mean FeO_x/BC concentration ratios for mass and number. (c) The number fraction of dust-like FeO_x for FeO_x particle sizes in the D_m domain of 170 nm≤D_m≤270 nm. The error bars in c represent uncertainty estimated by assuming that the number of particles detected in each altitude range follows a Poisson distribution. (d) The log(C_s-be/C_s-oi) value of BC-containing particles for BC particle sizes in the D_m domain of 205 nm≤D_m≤220 nm. The error bars in d represent the ±1σ ranges of single-particle data.

Mass-absorption cross-sections

An original discrete-dipole approximation code was used to calculate the mass-absorption cross-sections of the BC- and FeO_x-containing particles by assuming the particle shape, mixing state and refractive index of each material. Before discussing the results, we briefly explain the nontrivial assumptions used. For both BC and FeO_x, the particle shape was assumed to be a fractal-like aggregate of spherical monomers. The mixing state of the aggregate with other non-absorbing materials was assumed to be either bare or coated: the bare state denotes a pure aggregate, whereas the coated state denotes an aggregate coated by a large amount of non-absorbing material. In the coated state, the volume of the coating material was prescribed such that the theoretical log(C_s-be/C_s-oi) value of the model BC-containing particles was greater than the measured log(C_s-be/C_s-oi) values of the real BC-containing particles, where the C_s-be denotes the scattering cross-section of a BC-containing particle in the SP2 laser beam before the onset of evaporation²⁸ (see the Methods section). The mean+1σ value of the observed log(C_s-be/C_s-oi) value for BC-containing particles with D_m ∼200 nm was ∼0.9 at all altitudes below 8 km (Fig. 5d). On the basis of this observation, the coating/aggregate volume ratio in the coated state was chosen to be approximately 3–4 so that the theoretical value of log(C_s-be/C_s-oi) for the coated BC was ∼1.0 at D_m ∼200 nm (Supplementary Fig. 6).

The mass-absorption cross-sections (σ_a) calculated for the BC- and FeO_x-containing particles as functions of wavelength (λ) and mass-equivalent diameter D_m are shown in Fig. 6. In general, the σ_a value at a particular (λ, D_m) is approximately three times larger for BC than for FeO_x, primarily because the assumed bulk density of FeO_x (5.17 g cm⁻³) was 2.87 times greater than that of BC (1.8 g cm⁻³). Compared with the bare state, σ_a was enhanced by a factor of approximately two in the coated state because of the so-called lensing effects³⁰. The σ_a(λ, D_m) of FeO_x did not change appreciably with λ in the D_m domain largely contributing the total ambient FeO_x mass concentration. Thus, we expect that the atmospheric absorption coefficient (b_abs) attributable to anthropogenic FeO_x depends little on the wavelength. In contrast, the b_abs attributable to carbonaceous aerosols (BC+BrC) is known to decrease sharply with wavelength^14,15,31. This large difference in the wavelength dependence of b_abs between anthropogenic FeO_x and carbonaceous aerosols will be useful to classify them in remote sensing observations.

Figure 6: Mass-absorption cross-sections of black carbon- and iron oxide_x-containing particles as a function of wavelength and mass-equivalent diameter.

The mass-absorption cross-section σ_a(λ, D_m) (m² g⁻¹) are computed for four distinct particle types: (a) bare BC, (b) bare FeO_x, (c) coated BC, and (d) coated FeO_x.

Shortwave atmospheric heating

Here we quantify the instantaneous shortwave atmospheric heating attributable to FeO_x and BC using their observed size-resolved mass concentrations and theoretical mass-absorption cross-sections. The contribution to shortwave atmospheric heating from particles of a particular size is quantified by the size-resolved absorbing power dP_abs/dlogD_m (W m⁻³), which is defined as

where F_ac(λ) is the spectral actinic flux (W m⁻² nm⁻¹), σ_a(λ, D_m) is the mass-absorption cross-section (m² g⁻¹) and dM/dlogD_m (g m⁻³) is given by equations (2) or (3). The value of F_ac(λ) for each altitude was computed using a radiative transfer model assuming a clear sky and a daytime-mean solar zenith angle (see the Methods section). Figure 7 shows the size-resolved absorbing powers of BC and FeO_x in (a) dry PBL air and (b) at the highest altitude (6–8 km). The bell-shaped dP_abs/dlogD_m distributions for BC suggest that the BC-containing particles within the detectable D_m domain (70 nm≤D_m≤850 nm) predominantly contribute to the total BC absorption. On the other hand, the broad dP_abs/dlogD_m distributions of FeO_x suggest that the unobserved FeO_x-containing particles outside the detectable D_m domain (170 nm≤D_m≤2,100 nm) also contribute significantly to the total FeO_x absorption. Figure 7 also shows the theoretical transmission efficiency curves (Tr(D_m)) of our aerosol-sampling system for BC and FeO_x particles. The steeply decreasing Tr(D_m) curve for FeO_x suggests that we underestimated the dP_abs/dlogD_m value for FeO_x at D_m>∼600 nm.

Figure 7: Size-resolved shortwave absorbing powers dP_abs/dlogD_m of black carbon and iron oxide.

The dP_abs/dlogD_m values (thin lines) were calculated for two distinct atmospheric conditions: (a) dry PBL air (∼1 km altitude) and (b) the highest altitude (6–8 km). Solid and dashed lines represent the mean dP_abs/dlogD_m values for bare and coated particles, respectively. Error bars represent the variability in the dP_abs/dlogD_m values estimated from the ±1σ value of the 1-min data of dM/dlogD_m. In each panel, the transmission efficiency curves Tr(D_m) of the aerosol-sampling system calculated for BC and FeO_x are also shown (thick lines).

The total shortwave absorbing power P_abs (W m⁻³) was computed by integrating equation (4) over D_m:

where D_min and D_max denote the lower and upper limits of the detectable D_m domain, respectively. Figure 8a shows the altitude profiles of P_abs(BC) and P_abs(FeO_x). For both BC and FeO_x, P_abs values decreased monotonically with altitude, following their mass concentration profiles. For the dry PBL air, the mean P_abs(BC) and P_abs(FeO_x) values in the bare (coated) state were 4.0 (10.0) and 0.24 (0.44) mW m⁻³, respectively. These P_abs values are equivalent to atmospheric heating rates of 0.29 (0.72) and 0.017 (0.032) K per day, respectively. Figure 8b shows the altitude profiles of the mean P_abs(FeO_x)/P_abs(BC) ratio. In the boundary layer wherein anthropogenic FeO_x dominated the detected FeO_x particles (Fig. 5c), the mean P_abs(FeO_x)/P_abs(BC) ratio was estimated to be 0.04–0.07. The altitude dependence of P_abs(FeO_x)/P_abs(BC) was similar to that of the FeO_x/BC mass concentration ratio (Fig. 5b). In the highest altitude (6–8 km), wherein the dust-like FeO_x particles contributed 50% of the detected FeO_x particles (Fig. 5c), the P_abs attributable to anthropogenic FeO_x may be ∼50% of the calculated P_abs value.

Figure 8: Altitude profiles of the shortwave absorbing powers P_abs attributable to black carbon and iron oxide.

(a) The P_abs(BC) and P_abs(FeO_x), whereas b shows mean P_abs(FeO_x)/P_abs(BC) ratio. Filled and open circles (triangles) represent the mean values in each altitude range (dry PBL air) for the bare and coated particles, respectively. In a, error bars represent the variability in P_abs estimated from the ±1σ value of the 1-min mass concentration data.

It should be noted that the P_abs(FeO_x)/P_abs(BC) ratios reported herein are lower than the real values because our measurements underestimated the ambient FeO_x mass concentration in the D_m domain of ∼600 nm≤D_m≤2,100 nm and totally ignored the FeO_x particles outside the detectable D_m domain. Considering these facts, the real P_abs(FeO_x)/P_abs(BC) ratios in the boundary layer would be as large as 0.1.

Discussion

On the basis of the previous observations of BrC near Beijing in March 2013 (refs 11, 14), we roughly estimate the typical P_abs(BrC)/P_abs(BC) value in the boundary layer to be ∼0.1. Thus, we expect that P_abs(FeO_x) is as large as P_abs(BrC) in the East Asian continental outflows. In future studies, it is highly desirable to use an aerosol-sampling system with a higher transmission efficiency of large FeO_x particles. In addition, some technical improvements in the SP2 are necessary to perform FeO_x measurements beyond the current detectable D_m domain (170 nm≤D_m≤2,100 nm). Despite the room for such improvements, an essential conclusion can be drawn from our results, namely, in addition to BC and BrC, airborne FeO_x in the form of aggregated magnetite nanoparticles should also be recognized as a significant anthropogenic contributor to shortwave atmospheric heating.

In addition to clear-sky shortwave absorption, we briefly discuss some other potential climate effects of anthropogenic FeO_x particles. Low stratiform clouds are of frequent climatological occurrences over the mid- to high-latitude ocean and southeast China³². Under modest maximum supersaturation in such clouds, particle’s mass fraction activated to cloud droplets will be substantially larger for FeO_x than BC, because the critical supersaturations of FeO_x-containing aerosols will be lower than those of BC due to the larger D_m. Under these situations, P_abs(FeO_x)/P_abs(BC) is enhanced because the lensing effect in droplets³³ is larger for FeO_x than for BC. We expect that the number of co-emitted cloud condensation nuclei and their precursor gases in anthropogenic FeO_x-rich sources, which remain uncertain, are substantially different from those in anthropogenic BC- and BrC-rich sources such as residential coal, industrial coal and biomass fuels⁷. Thus, without comprehensive investigations, it is not clear whether the net positive climate forcing of anthropogenic FeO_x-rich sources is negligible or comparable with those of BC- and BrC-rich sources. Finally, it should be mentioned that the anthropogenic FeO_x particles may also play a role in the biogeochemical cycles³⁴.

Methods

Modified single-particle soot photometer

A modified single-particle soot photometer (SP2), which detects light-absorbing refractory aerosols on the basis of intra-cavity laser-induced incandescence, was used to measure BC and iron oxide FeO_x particles²⁵. The BC and FeO_x are discriminated from each other on the basis of the colour ratio, which is an indicator of the boiling temperature²⁵, and the peak amplitude of the blue-band incandescence signal, which is an indicator of an incandescing particle’s size²⁵. Supplementary Figure 7 shows a scatterplot of the peak amplitudes and colour ratios of all incandescing particles detected during the A-FORCE 2013 W campaign. The boundary lines for discriminating FeO_x from BC were determined on the basis of experimental results²⁵ and are also shown in Supplementary Fig. 7.

The masses (m) of the individual FeO_x and BC particles were determined from the peak heights of the incandescence signals using the experimentally determined mass-to-peak height relationships²⁵. The mass-equivalent diameters (D_m) for BC and FeO_x were calculated from the observed masses m assuming the void-free densities of 1.8 g cm⁻³ and 5.17 g cm⁻³ (density of magnetite), respectively. In this study, the detectable size domains of the BC and FeO_x particles were 70 nm≤D_m≤850 nm and 170 nm≤D_m≤2100, nm, respectively.

The detailed morphological properties of the individual particles containing a particular mass of each incandescing material were evaluated on the basis of time-resolved scattering cross-sections C_s (integrated over the solid angle of light collection) in a laser beam derived from the scattering signal^35,36,37. We evaluated whether the incandescing material (BC or FeO_x) was attached to the surface of another particle on the basis of the magnitude of C_s at the onset of the incandescence signal (C_s-oi)²⁸. In our observations, the fraction of the attached-type²⁸ BC-containing particles with D_m=200 nm was <3% at any altitudes below 8 km. Thus, we assumed the C_s-oi of BC-containing particles to be equivalent to the C_s of BC core. For BC-containing particles, the ratio of C_s before the onset of particle evaporation (C_s-be) to C_s-oi is used as an indicator of the amount of non-refractory material (for example, sulfate) coating the BC²⁸.

The timing of the onset of the incandescence signal (t_oi) is an indicator of the heating rate of an absorbing particle in the laser beam of the SP2; t_oi tends to be lower (earlier) for more efficient light absorbers. Our experiments²⁵ showed that the t_oi of magnetite (Fe₃O₄) particles was markedly earlier than that of hematite (α-Fe₂O₃), reflecting the greater absorption efficiency of magnetite. It should be noted that comparisons of t_oi values derived from the laboratory experiments and field data are meaningful only under similar SP2 conditions (that is, the same laser power and width of the Gaussian beam) because the t_oi of a particular composition depends on these parameters. Supplementary Fig. 4 shows the t_oi distributions for the ambient BC and FeO_x particles in dry PBL air. The SP2 laser power during the A-FORCE 2013W campaign was similar to that in our laboratory experiments²⁵, as expected from the comparison of t_oi−t_cen (0.2 μs) for BC between ambient data (−55<t_oi−t_cen<−30) and our laboratory results (−50<t_oi−t_cen<−35; refer to Fig. 2c of Yoshida et al.²⁵). The t_oi distributions of the FeO_x particles for two different mass ranges (10 fg≤m≤88 fg and m > 530 fg) were similar, indicating that the primary FeO_x material does not change appreciably with FeO_x particle mass (Supplementary Fig. 4). Our experiment illustrated that the incandescing probability of pure hematite particles was zero for m<∼100 fg because the absorption efficiency of hematite particles in this size range is insufficient to heat the particles to the incandescing temperature²⁵. Thus, the incandescing FeO_x particles in dry PBL air had greater absorption efficiency than hematite. The t_oi−t_cen (0.2 μs) distributions of FeO_x with 10 fg≤m≤88 fg and m>530 fg largely overlapped with the t_oi−t_cen distributions of pure magnetite with m <∼100 fg (−40<t_oi−t_cen<−20)²⁵ and m>530 fg (−40<t_oi−t_cen<−30)²⁵, respectively. On the basis of these results and the EELS spectra shown in Supplementary Fig. 3, the major constituent of the detected FeO_x particles in dry PBL air is likely magnetite (Fe₃O₄).

Electron microscopy analysis

An aerosol impactor-sampler¹⁹ onboard the aircraft was used to collect aerosol samples on the Cu TEM grids with collodion substrate at 12-min intervals during each flight. A 120-kV transmission electron microscope (JEM-1400, JEOL) equipped with an energy-dispersive X-ray spectrometer (Oxford Instruments) was used for the STEM–EDS analysis. A 200-kV transmission electron microscope (ARM 200, JEOL) was used for the EELS analysis.

Transmission efficiency of aerosols

Special care is required to measure the number concentrations of supermicron-sized aerosols in ambient air as the transmission efficiency (Tr) through the entire tubing apparatus connecting the aerosol inlet to the particle detection volume unavoidably decreased because of the inertial and gravitational depositions. Since a substantially large fraction of the total FeO_x mass was expected to be in the supermicron size range (Fig. 4), our conclusion is strongly dependent on the degree of sampling loss of large FeO_x particles. According to the theoretical formulae compiled by Pramod et al.³⁸, we estimated the Tr(D_m) curves of the BC and FeO_x particles through our aerosol-sampling system. In our A-FORCE 2013W aircraft campaign, the sample air was aspirated through a forward-facing shrouded inlet (DMT Inc., Boulder, CO, USA) installed on the top of the aircraft fuselage. This aerosol inlet is a replica of ‘University of Hawaii shrouded solid diffuser inlet’ described and evaluated by McNaughton et al.³⁹. An isokinetic aspiration was performed to maintain the aspiration efficiency at ∼1.0, independent of the particle size. The geometric specifications and flow rates of the tubing apparatuses comprising the sampling system are listed in Supplementary Table 1. The void-free densities of BC and FeO_x were assumed to be 1.8 and 5.17 g cm⁻³, respectively. The dynamic shape factors of the BC and FeO_x particles were assumed to be 1.5. Our experiment using pure magnetite particles confirmed the reasonable agreement between the measured and theoretical Tr(D_m) curves for a ¼-inch tube (length=0–3 m) assuming the same density and dynamic shape factor.

Computing the fractal-like aggregates of spheres

On the basis of the TEM observations of ambient BC- and FeO_x-particles, we assumed the shapes of the model BC- and FeO_x-particles for the electromagnetic scattering calculations to be fractal-like aggregates of spheres. The aggregate geometry was computed using an original tunable cluster–cluster aggregation (CCA) code called aggregate_gen, which is an efficient C++ implementation of the hierarchical CCA algorithm⁴⁰. We assumed the fractal prefactor and fractal dimension to be k_f=1.0 and D_f=2.8, respectively. The monomer diameters of BC and FeO_x were assumed to be 40 and 80 nm, respectively. The number of monomers N_pp in the aggregates ranged from 8 to 16384. This range of N_pp covers the observed D_m domain for both BC and FeO_x.

Electromagnetic scattering calculations

The mass-absorption cross-sections for BC and FeO_x as functions of both wavelength λ and mass-equivalent diameter D_m were computed using an original electromagnetic scattering solver called block-DDA, which utilizes the block Krylov subspace methods^41,42,43 to efficiently solve the discrete-dipole approximation (DDA)^44,45 for multiple incident waves, or equivalently, for multiple target orientations. For every (λ, D_m) condition, we used the mean value of the results from four different randomly chosen target orientations. When applying block-DDA to the scattering problems for fractal-like aggregates of BC or FeO_x monomers, the monomer dipole formulation was assumed to avoid the discretization shape error in each monomer⁴⁶. The coupled electric and magnetic dipoles formulation^46,47 was used to mitigate the multipole-truncation error associated with the monomer-dipole assumption⁴⁶. To apply efficient fast Fourier transform-based algorithms in DDA⁴⁸, we adjusted the center position of each monomer dipole to the nearest site of the computational cubic lattice (CL)⁴⁶. In this study, the lattice spacing of the CL was set to one-half of the monomer diameter. The geometry of the coating material surrounding a fractal-like aggregate of spheres was computed by the following procedure. First, the CL sites in the proximity of each monomer (at a distance of less than T monomer radii) were selected as candidates for coating volume elements, where the coating thickness was controlled by the parameter T. In this study, T was selected to be 3. Next, the CL sites overlapping with monomer volume, if present, were removed from the candidates. Finally, the coating volume was assigned to each candidate.

We used the complex refractive indices of BC and FeO_x listed in Supplementary Table 2. Ackerman and Toon⁴⁹ also used this refractive index data set to estimate the radiative effects of atmospheric aerosols containing BC and magnetite. The refractive index of the coating material was assumed to 1.5+0.0i, independent of the wavelength.

Radiative transfer calculations

The spectral actinic fluxes F_ac(λ) under the A-FORCE 2013W condition were calculated using the radiative transfer software package libRadtran (version 1.6 beta)⁵⁰. At each altitude, the absorption coefficients b_abs(λ) (m⁻¹) of the BC and FeO_x particles were calculated on the basis of observational D_m/dlogD_m data and theoretical σ_a(λ, D_m) values. The extinction coefficients b_ext(λ) were evaluated as b_abs(λ)/(1−ω), where the single-scattering albedo ω was assumed to be 0.85, independent of λ. The Henyey–Greenstein function with asymmetry parameter 0.7 was used for the scattering phase function. Shortwave absorption by gases (for example, water vapour and ozone) was calculated using the LOWTRAN/SBDART parameterization assuming the default atmospheric profiles (US-standard atmosphere). Effective solar zenith angle averaged over the daylight hours (local noon±6 h) was assumed for the actinic flux calculation. All the input parameters used in the radiative transfer calculations are listed in Supplementary Table 3.

Cloud and aerosol spectrometer probe

A cloud and aerosol spectrometer probe (CAS; Droplet Measurement Technologies, Inc.) installed under a major wing of the aircraft was used for measuring size-resolved number concentration of total aerosol in the light-scattering diameter (D_p) domain of 0.5 μm≤D_p≤50 μm. The CAS instrument used herein was also described in Koike et al.⁵¹.

Code availability

The aggregate_gen code for computing the fractal-like aggregates of spheres and the block-DDA code for electromagnetic scattering were developed by the corresponding author (N.M.) and are available in the GitHub repository at: https://github.com/nmoteki.

Data availability

FeO_x data shown in Fig. 4 is available at NOAA National Center for Environmental Information (NCEI) Reference ID: YKJKWD. The other observational data and calculation results are available from N. Moteki.