In-situ dust measurements were conducted in a mineral dust emission area in southeastern Morocco, at the transition between the southern foothills of the Atlas Mountains and the Saharan Desert. The study site is located in L'Bour (29°49′30′′ N, 5°52′25′′ W, 500 m a.s.l.), a small terminal depression near the Drâa dry riverbed, west of M'Hammid El Ghizlane. It is also located east of Lake Iriki, a seasonal dry lake, and adjacent to two major dune fields, Erg Cheggaga and Erg Smar (see Fig. ). The surface at L'Bour is predominantly hard-packed, with crust formations resulting from wind and dust erosion, interspersed with shallow sand dunes less than 1 m high. These characteristics make the site well suited for studying freshly emitted dust from natural Saharan sources. Further details on the sampling site and its environmental conditions can be found in , , .

Scattering coefficients (bscat) were measured using an LED-based multi-angle integrating nephelometer (Aurora 4000, ECOTECH Pty Ltd, Knoxfield, Australia). The nephelometer operates at three wavelengths (450, 525 and 635 nm) and collects scattering measurements at eight angular positions (0, 10, 25, 40, 55, 70, 90, and 170°). The forward (0°) and backward (170°) scattering measurements are affected by non-ideal illumination of the light source and by angular truncation . For the descriptive analysis of the campaign optical properties, the scattering coefficients were corrected using the correction scheme of . These corrected scattering coefficients are used throughout the manuscript when reporting scattering coefficients, SSA, asymmetry parameter, backscatter fraction, and mass scattering and extinction efficiencies, except in the retrieval of the imaginary part of the complex refractive index.

For the refractive-index retrieval, we use the uncorrected scattering coefficient measured by the nephelometer, hereafter referred to as the truncated scattering coefficient, (tsca). The corresponding truncated scattering is simulated explicitly in the retrieval observation operator, as described in Sect. . This distinction avoids applying a correction from truncated scattering to full-phase-function scattering in the retrieval itself, which would introduce additional assumptions about particle shape and the scattering phase function. The corrected multi-angle scattering measurements were also used to derive the asymmetry parameter e.g.,.

Multi-wavelength aerosol absorption coefficients (babsλ) were derived using a multi-wavelength dual-spot aethalometer (AE33, Aerosol d.o.o.]). The AE33 measures light attenuation at seven wavelengths spanning from the short-UV to the near-infrared range (370, 470, 520, 590, 660, 880, and 950 nm). Absorption coefficients are then computed using Eq. (17) of , with the AE33 online correction for filter loading effects.

The uncertainty of AE33-derived absorption coefficients, ranging between 10 % and 30 %, depends strongly on measurement artefacts, particularly the cross-sensitivity of attenuation measurements to light scattering by particles on the filter . To correct for this effect, we used the scattering artefact parameter, ms, and the multiple-scattering parameter, C, reported in for Saharan mineral soil samples collected during the same FRAGMENT-Morocco campaign and later resuspended and analysed under controlled laboratory conditions. This correction is therefore directly linked to the dust sampled in the present study. To account for uncertainty in the AE33 multiple-scattering correction, we performed a sensitivity analysis using lower and upper values of C derived from the 95 % confidence interval reported in Table S2 of . This sensitivity results in an uncertainty of approximately 5 % in the corrected absorption coefficient at 370 nm, increasing up to approximately 20 % at 950 nm, where the relative contribution of the scattering artefact is larger for mineral dust.

The aethalometer and nephelometer shared the same sampling line and inlet, with the AE33 pump also used for the nephelometer. During the initial phase of the L’Bour field campaign (4 September 2019 00:00 UTC to 26 September 2019 12:00 UTC), in-situ surface optical measurements were performed using a PM_2.5 inlet. After this date and until the end of the campaign (1 October 2019 08:00:00 UTC), a PM₁₀ inlet was used instead. To reduce instrumental noise while accounting for all significant turbulent structures carrying momentum flux, all the campaign measurements, including the optical measurements, were averaged into 15 min intervals for analysis .

Number PSDs were measured using a Fine Dust Aerosol Spectrometer (Fidas 200S, Palas GmbH). The instrument measured aerosol particles in 63 logarithmically spaced optical-diameter bins between 0.20 and 19.1 µm, with a time resolution of 120 s and an inlet flow rate of 4.8 Lmin-1. The first three bins were not used as they were found to be unreliable cf.. As for the optical measurements, the Fidas data were averaged into 15 min intervals. The Fidas was calibrated in the field using polystyrene latex spheres (PSLs) with a CRI of 1.59+0i. Because mineral dust particles are non-spherical and absorbing, the PSL-calibrated optical diameters must be converted into dust geometric diameters before deriving mass concentrations and optical simulations. We applied the optical-to-geometric diameter conversion described in Sect. , following the method of . This conversion depends on the assumed particle shape and CRI. For the descriptive analysis in Sect. , mass-dependent quantities were computed using the converted PSD assuming spherical particles and an initial dust refractive-index range guided by , with real parts between 1.47 and 1.52, and imaginary parts between 0.0001 to 0.005. The retrieval of k under different prescribed assumptions for particle shape, particle density, and n is described separately in Sect. .

From the converted number PSD, we calculated the mass concentration (MPM) and the effective radius (Reff). MPM is given by the third moment of the number PSD, 1MPM=ρ⋅π6⋅∫D0DxD3N(D)dD; where D is the particle geometric diameter, and N(D) is the dust number PSD for diameter D derived from the Fidas, and ρp is the particle density. Here, we have used a density of 2.15 gcm-3. This value was obtained from samples collected at the site and analysed with a helium pycnometer. The method estimates particle density from the volume accessible to helium gas, including open and semi-open pores. The results indicate that particle density decreases with decreasing particle size, reaching approximately 2.15 gcm-3 for particles with diameters below 20 µm. This value is therefore considered a reasonable approximation for the dust particles sampled in the size range relevant to this study.

Reff is then defined as the ratio of the third to the second moment of the number PSD, 2Reff=12⋅∫0.2DxD3N(D)dD∫D0DxD2N(D)dD; Here, D spans the relevant diameter range between the first valid bin D0 and and the upper diameter Dx. The value of Dx corresponds to the geometric diameter associated with the aerodynamic diameter of the inlet cut-off, and therefore differs between the PM_2.5 and PM₁₀ periods. The optical-to-geometric diameter conversion is described in Sect. . For ρp=2.15 gcm-3 and spherical particles, the PM_2.5 aerodynamic cut-off corresponds to a geometric diameter of 1.72 µm.

Wind measurements were conducted at 2 m above ground level, approximately matching the inlet heights of the optical and PSD instruments. We used a 2-D sonic anemometer (model WINDSONIC4-L, Campbell Scientifics, USA) mounted on a 10 m meteorological tower. As described in , the tower also hosted multiple 2-D and 3-D sonic anemometers, whose data were processed to derive turbulence parameters, including the wind friction velocity (u*). Briefly, u* was obtained via an iterative law-of-the-wall approach, which assumes a (pseudo)logarithmic wind profile in the atmospheric surface layer cf.and references therein. Although the raw wind data were recorded at 0.5 Hz, they were averaged into 15 min intervals during post-processing to facilitate a stable, consistent comparison with the other collocated instruments.

By combining the extensive optical properties, namely absorption from the AE33 and corrected scattering from the polar nephelometer, with the PSDs from the Fidas, we derived time series of intensive optical properties. These properties depend on the physico-chemical properties of the dust particles, including mineralogy, PSD, and shape. Unless otherwise stated, the scattering coefficients used in this section are the corrected scattering coefficients described in Sect. . The truncated scattering coefficient, tsca, and the corresponding truncated single scattering albedo, tssa, are used only for the retrieval of k described in Sect. .

a.

The Scattering Ångström Exponent (SAE) mostly reflects particle size, with values less than 1 typically indicating coarse particles , as often observed during fresh dust emissions in our campaign. SAE was calculated by fitting the scattering coefficients at three nephelometer wavelengths in a log-log space, 3ln⁡(bscat(λ))=-SAE⋅ln⁡(λ)+β. where β is the intercept of the linear fit.

The imaginary part of the complex refractive index (k) was retrieved by comparing measured and simulated optical properties during PM_2.5 dust-event periods. The retrieval uses the truncated scattering, denoted tsca in Sect. , together with the AE33 absorption coefficient, babs. We define the truncated SSA, tssa, as: 7tssa=tscatsca+babs.

The value of k is then obtained by minimizing the difference between simulated and measured tssa. The optimization is performed under prescribed assumptions for particle shape, particle density, and real part of the refractive index, n. Therefore, the retrieval should be interpreted as a conditional estimate of k for each set of assumptions, rather than as a simultaneous retrieval of particle shape, PSD, n, and k.

Our methodology builds on the approach of , but introduces two main modifications motivated by the characteristics of our field dataset. First, the conversion of Fidas optical diameters into dust geometric diameters is coupled to the retrieval, because this conversion depends on the assumed particle shape and refractive index. Second, the cost function is based on tssa, rather than on absorption and scattering coefficients separately. This choice reduces the sensitivity of the retrieval to possible differences in the absolute particle number sampled by the OPC and by the optical instruments, and avoids correcting the measured truncated scattering coefficient to a full-phase-function scattering coefficient in the retrieval itself.

The retrieval procedure consists of three main steps: (1) converting the Fidas PSL-calibrated optical diameters into dust geometric diameters under prescribed assumptions for particle shape, density, and real refractive index; (2) simulating truncated scattering and absorption coefficients from the converted PSD; and (3) minimizing a quadratic cost function based on tssa. A general overview of the procedure is shown in Fig. .

A key aspect of the method is that the OPC diameter conversion and the optical simulations are treated consistently. The transformation from PSL-based optical diameters to dust geometric diameters depends on k, while absorption is also directly controlled by k. To avoid a sequential inconsistency, we first retrieve the value of k at the wavelength relevant for the OPC diameter conversion. This value is then used to convert the Fidas optical diameters into dust geometric diameters. The resulting dust PSD is subsequently used to retrieve k at the AE33 wavelengths.

Another key difference from the method of is that we define the cost function directly in terms of the truncated single scattering albedo, tssa. This choice is motivated by both the sensitivity of tssa to k, and the sampling configuration of the field experiment. The Fidas measured number size distributions through its own inlet over a broad optical-diameter range of approximately 0.2–19 µm, whereas the AE33 and nephelometer sampled through a common PM_2.5 inlet during the period used for the refractive-index retrieval. To make the Fidas PSD representative of the aerosol population sampled by the optical instruments, we applied an effective PM_2.5 cut-off to the converted dust geometric PSD. However, this procedure cannot fully reproduce possible differences in inlet transmission, size-dependent particle losses, counting efficiency, or absolute particle number between the Fidas and the optical sampling line.

We tested alternative cost functions incorporating additional information from the polar nephelometer, including a combined cost function based on quadratic departures of tssa and the backscatter fraction (BF). However, our dataset did not support a robust simultaneous retrieval of both n and k. This limitation likely reflects the combined sensitivity of the inversion to particle-shape assumptions, particle density, inlet cut-off modelling, and PSD uncertainties. In particular, closure tests comparing inversions based on tssa with inversions using extensive optical properties suggested a possible mismatch between the OPC-derived PSD and the aerosol population sampled by the nephelometer and aethalometer. Expressed as an equivalent scaling of the OPC particle number concentration, this mismatch was of the order of at least 20 % in some cases. Moreover, the spectral dependence of the required scaling suggests that the mismatch may not be a simple constant offset in particle number, but may include size-dependent effects, with larger discrepancies towards the lower and upper ends of the PM_2.5 size range.

Because the available data do not allow this potential mismatch to be quantified robustly, we use tssa in the cost function instead of relying on inversions based directly on extensive absorption and scattering coefficients. Since tssa is a ratio between truncated scattering and extinction, it is less sensitive to absolute concentration differences between the OPC and the optical instruments, while retaining sensitivity to the absorption-to-scattering balance controlled by k. The drawback is that tssa is not sufficiently sensitive to n to support a robust simultaneous retrieval of n and k from this dataset. We therefore retrieve k for a range of fixed n values representative of mineral dust, rather than solving for both parameters simultaneously. As shown in Sect. , the retrieved k is only weakly sensitive to the prescribed value of n within the plausible range tested here.

The campaign was characterized by generally calm conditions in the early morning, followed by stronger winds in the afternoon, leading to frequent local saltation and dust emission. This pattern followed the diurnal cycle of solar heating and the resulting destabilization of the near-surface atmospheric layers. On some days, the site was also influenced by the advection of local anthropogenic particles from up-river villages. Additionally, two intense cold pool outflows, referred to as haboobs, were recorded on 4 and 6 September 2019. A more detailed discussion of the atmospheric conditions, turbulence parameters, and dust fluxes during the campaign is provided in .

Two distinct measurement periods were defined based on the inlet cut-off of the optical measurements. During the initial part of the campaign, from 4 until 26 September 2019, the AE33 and nephelometer sampled through a PM_2.5 inlet. From 26 September 2019 until the end of the campaign, the optical measurements were performed using a PM₁₀ inlet. Figure S1 in the Supplement shows the temporal evolution of extensive and intensive optical properties, mass concentration, particle effective radius, wind speed, and friction velocity throughout the full campaign.

To analyze the dominant aerosol conditions during the measurement period, we identified three representative periods. Period A corresponds to local dust emissions on 21–22 September 2019. Period B corresponds to the haboob-driven dust event on 6–7 September 2019). Period C corresponds to a period influenced by anthropogenic pollution advection mixed with minor local dust emissions between 7 and 11 September 2019. These periods are highlighted in Fig.  and are used to illustrate the range of optical conditions observed during the campaign.

Table summarizes the time-averaged optical properties for the PM_2.5 and PM₁₀ periods, distinguishing between the full measurements, dust-event conditions, and the three defined periods. Dust events during the PM_2.5 period were identified using three criteria: AAE above 1.5, SSAAE below 0, and a mass concentration above 10 µgm-3 . Extensive optical properties are presented with their standard errors. Intensive optical properties are summarized using two approaches: (i) averages weighted by the corresponding extensive properties, namely absorption, scattering, or extinction, and (ii) direct time averages with standard errors. The weighted averages emphasize conditions with elevated dust concentrations, while the direct averages reflect the overall variability of the campaign. This variability is further showcased in Figs. S2–S5.

Period A, dominated by local dust emissions, closely resembles the average dust-event conditions, confirming that such local emissions were common during the campaign. Period B, corresponding to the haboob event, exhibits intensive optical properties similar to those of Period A, but with substantially larger extensive optical properties because of the higher dust mass concentration. In contrast, Period C, influenced by anthropogenic pollution, shows lower mass concentrations and therefore lower absorption and scattering coefficients. However, it exhibits much higher mass absorption efficiencies, particularly at longer wavelengths. This indicates an enhanced contribution of fine carbonaceous particles to the optical properties measured at the site during periods of weak easterly winds and limited local dust emission, as discussed in .

Figure and Table  show that, during PM_2.5 dust events, SSA was around 0.955 at 370 nm, 0.985 at 550 nm and 0.993 at 880 nm. During the same conditions, AAE values were elevated relative to anthropogenic-dominated periods and SAE remained below 0, consistent with a dominant contribution from coarse mineral dust and stronger absorption at shorter wavelengths. Brief decreases in effective radius, accompanied by dips in SSA and increases in SAE and k, again suggest intermittent advection of anthropogenic particles to the site during periods of low wind speed and weak dust emission. For PM₁₀, SSA was lower, with values of approximately 0.945 at 370 nm, 0.974 at 550 nm and 0.984 at 880 nm (Table ).

A comparison with other dust SSA measurements compiled in Table S1 of the Supplement of shows that our PM₁₀ results lie well within the general range of Saharan dust SSA values reported by previous in-situ and remote-sensing studies. For example, reported an SSA of 0.957 at 537 nm in Tinfou and reported an SSA of 0.971 at 550 nm in Western Sahara. As expected, the PM_2.5-based values in our study, which emphasize the finer fraction of the sampled dust, tend towards slightly higher SSA at near-infrared wavelengths.

Many of the measurements compiled in correspond to broader particle size ranges or to long-range transported dust. Such differences can affect SSA because the inclusion of coarser particles, as well as changes in particle morphology, composition, and mixing state during transport, can modify both scattering and absorption. The effect of particle size on the optical properties measured during our campaign is analysed in Sect. . Furthermore, Table S1 of includes dust sampled across regions where mineralogy and aerosol mixing states can differ from freshly emitted dust in southeastern Morocco, including samples collected far from the source area, such as Cabo Verde, the Canary Islands, the Atlantic outflow and the West Mediterranean. Therefore, although our SSA measurements overlap with previous studies, differences in size range, sampling region, dust ageing, and possible aerosol mixing must be considered when comparing results across datasets.

The mass efficiencies, MAE, MSE and MEE, show strong sensitivity to inlet cut-off, with variations approaching 1 order of magnitude. This is expected because most of the mass concentration of mineral dust is found in the coarse fraction. The MAE obtained here for dust events during the PM_2.5 period, 0.141 m2g-1 at 370 nm, is higher than the value reported by for Saharan dust, 0.107 m2g-1 at 375 nm, but much lower than the value reported for Sahelian dust, 0.711 m2g-1 at 375 nm. This comparison illustrates the large variability in dust absorption efficiency across source regions. The asymmetry parameter and backscatter fraction during dust events, 0.68 and 0.11, respectively, fall within the range reported by for Saharan dust, 0.71 ± 0.03 and 0.094 ± 0.014, respectively.

The higher absorption efficiency in Period C is associated with the presence of anthropogenic aerosol particles, particularly between midnight and noon. This interpretation is supported by AAE and SAE values close to 1, SSAAE values close to zero or positive, a significantly smaller effective radius compared with dust events (0.42 vs 0.54 µm), and a lower SSA at 880 nm of around 0.978. Together, these features indicate a larger relative contribution of fine particles and a higher apparent imaginary refractive index (k). Consistently, the mass absorption efficiency strongly increases from ∼ 0.141 to ∼ 0.182 m2g-1.

Although the average of the full measurement period exhibits a clear dust optical signature, the overall values are influenced by Period C. This highlights that, even at remote and relatively isolated dust-source locations, regional and local anthropogenic pollution can impact dust measurements.

The last row of Fig.  illustrates the temporal evolution of the conditionally estimated imaginary part of the complex refractive index (k) at 520 nm for different shape assumptions: spheres (solid line), triaxial ellipsoids (dashed line), and the full range of values considered in Sect. . During dust events, k remains relatively stable. In contrast, Period C and the calm wind phases of Periods A and B exhibit increases of up to an order of magnitude, highlighting the stronger influence of absorbing anthropogenic particles during these periods. These k estimates are used here diagnostically to interpret the temporal variability of the optical measurements; the dust-event retrievals and their sensitivity to particle shape, density, and n are analysed in detail in Sect. .

At this measurement site, mineral dust fluxes, wind friction velocity, and particle size are closely interrelated . In this section, we examine the dependence of the observed optical properties on the effective radius (Reff, cf. Sect. ). The campaign consisted of two distinct measurement periods with different inlet cut-offs (PM_2.5 and PM₁₀), which strongly influence Reff because coarse particles dominate the mass during dust emission events.

Figure shows the absorption (babs) and scattering (bscat) coefficients, and SSA as a function of Reff. The upper panels correspond to the PM_2.5 period, while the lower panels represent the PM₁₀ period. The average absorption Ångström exponent, AAE, for each Reff bin is also indicated in Fig. a, d. For the PM_2.5 period, babs exhibits a non-monotonic U-shaped dependence on Reff, initially decreasing until the 0.4–0.5 µm bin and then increasing again for larger sizes. In contrast, bscat increases consistently for Reff>0.4 µm. SSA also shows a clear dependence on Reff, with distinct spectral behaviour between the PM_2.5 and PM₁₀ periods.

The observed U-shaped dependence in babs during the PM_2.5 period reflects the varying contribution of different aerosol sources. The smallest Reff values, associated with AAE values close to ∼ 1, correspond to periods influenced by transported fine carbonaceous particles. Larger Reff values are associated with local and nearby dust emissions. At small Reff, babs decreases as SSA increases, despite similar bscat values across the first three size bins. For larger Reff, SSA stabilizes and both bscat and babs increase with dust abundance. The spectral dependence of SSA during the PM_2.5 period is therefore consistent with a transition from conditions influenced by fine carbonaceous aerosols to conditions dominated by coarser mineral dust. The PM₁₀ period exhibits optical behaviour consistent with the larger Reff bins of the PM_2.5 period, reflecting the stronger influence of coarse dust when the broader inlet cut-off is used.

The spectral dependence of babs, bscat, and SSA is further examined through their respective Ångström exponents (Fig. ). The AAE increases with effective radius, particularly during the PM_2.5 period for Reff values above 0.45 µm, where it rises from approximately 1.2 to 2.5. This trend is consistent with increasing friction velocity (u*), indicating stronger local dust emissions. Figure S7 further shows that, during the PM_2.5 period, the increase in Reff with wind friction velocity is also associated to higher AAE values. In contrast, during the PM₁₀ period, AAE exhibits a more gradual and steady increase with Reff and u*.

Figure also shows a strong relationship between SAE and effective radius. During the PM_2.5 period, SAE decreases markedly, from around 1 to values near or below 0, as Reff increases. During the PM₁₀ period, the high concentration of coarse particles keeps SAE consistently low, generally below 0. Negative SSAAE values have been recognized as a key indicator of Saharan dust events over Europe , and this criterion is met for Reff values above 0.3 µm during the PM_2.5 period and throughout the PM₁₀ period.

Overall, these results indicate that during the PM_2.5 period, when the optical instruments sampled a narrower size range, the dust optical signature became prominent mainly under strong emission conditions. In contrast, during the PM₁₀ period, the inclusion of coarser particles produced a consistently stronger dust optical signature, significantly shaping the spectral behaviour of babs, bscat, and SSA.

Additionally, we computed two key parameters related to the scattering phase function of the particles: the backscatter fraction (BF) and the asymmetry parameter (g). As shown in Fig. , forward scattering increases with particle effective radius (Reff), as indicated by increasing g and decreasing BF. The spectral dependence of these parameters also shifts as particles become coarser, with the slope of BF transitioning from negative to positive and that of g from positive to negative. This indicates that finer particles exhibit stronger backscattering at shorter wavelengths, while coarser particles favour forward scattering. During the PM_2.5 period, for Reff>0.4 µm, BF decreases from approximately 0.10 at the shortest nephelometer wavelength to approximately 0.09 at the longest wavelength, while g increases from approximately 0.68 to 0.70. During the PM₁₀ period, these spectral variations are less pronounced, with BF values ranging between 0.12 and 0.16 and g values between 0.6 and 0.64. These values are consistent with previous measurements of Saharan dust. reported (g) values from about 0.75 to 0.65 and BF values from about 0.07 to 0.12 for Saharan dust advected to the western Mediterranean, while reported (g≈0.74) from airborne measurements within the Saharan Aerosol Layer over the Atlantic.

Figure shows the mass absorption, scattering, and extinction efficiencies as a function of wavelength and Reff. Regardless of the inlet cut-off period, MAE consistently decreases as particle size increases. During the PM_2.5 period, noting the logarithmic y-axis, MAE values drop by approximately 1 order of magnitude, from ∼ 0.30 to 0.15 m2g-1 at short-UV wavelengths and from ∼ 0.12 to 0.02 m2g-1 at near-infrared wavelengths. This decrease reflects the lower absorption per unit mass of larger mineral dust particles compared with smaller particles, which include a greater relative contribution from fine absorbing aerosols during some periods.

The scattering and extinction efficiencies show a different behaviour across the two inlet cut-off periods. During the PM_2.5 period, MSE decreases with wavelength for particles with Reff below 0.4 µm, consistent with the stronger scattering efficiency of fine particles at shorter wavelengths . For coarser particles within the PM_2.5, MSE remains more similar across wavelengths. During the PM₁₀ period, MSE declines significantly, both compared to the PM_2.5 period and as Reff increases. This behaviour reflects the shifting peak scattering efficiency toward longer wavelengths, beyond the measurement range of this study . The combined effects of absorption and scattering efficiencies are captured in the MEE trend (Fig. c, f), which highlight the distinct optical behaviour of different aerosol regimes: fine particles exhibit higher MAE and MSE, whereas coarser dust particles contribute more strongly to total scattering.

This section presents the retrieval of the imaginary part of the complex refractive index, k, for mineral dust, following the methodology described in Sect. . The analysis focuses exclusively on periods classified as dust events (see Sect. ) during the PM_2.5 measurement period. As discussed in Sect. , the retrieved values should be interpreted as conditional estimates of k under prescribed assumptions for particle shape, particle density, the real part of the refractive index, and the AE33 absorption correction.

Two complementary retrieval averages are reported. First, k was retrieved independently for each 15 min dust-event interval. The temporal average of these retrievals is referred to as the average of the retrievals. Second, k was retrieved from the time-averaged dust-event measurements of truncated scattering, absorption, and PSD. This single estimate is referred to as the retrieval of the average. The first approach gives equal weight to each dust-event interval, whereas the second gives greater influence to periods with larger dust loading and more representative mean optical properties.

Figure  summarizes the retrieved k values. Panel (a) shows the spectral dependence of (k) for different particle-shape assumptions using the central values (n=1.49, ρp=2.15 gcm-3), and the central AE33 correction. The particle-shape assumptions include spheres (Sph), biaxial spheroids using the aspect-ratio distribution measured during the campaign by (Bi dist; including oblate and prolate spheroids), biaxial spheroids using the aspect-ratio distribution (Dub-06), triaxial ellipsoids with aspect ratio 1.46 and height-to-width ratio 0.45 (Tri), and the six irregular shapes A–F from . The values reported for the Moroccan sample by are also shown for comparison.

Panels (b)–(d) of Fig.  show the distributions of 15 min retrievals for spheres, biaxial spheroids, and triaxial ellipsoids. Each distribution includes the variability arising from the range of prescribed (n) values, from 1.48 to 1.55 in increments of 0.005, and the three particle-density assumptions, (ρp=1.8, 2.15, and 2.5 gcm-3). The red symbols indicate the retrieval of the average for the central value of (C), while the error bars show the variability introduced by the lower and upper (C) values derived from the 95 % confidence interval of . The corresponding results for the individual irregular shapes are provided in the Supplement.

Across the five shortest AE33 wavelengths, all shape assumptions show an overall decrease in k with increasing wavelength, consistent with stronger absorption by iron-bearing minerals at shorter wavelengths. The dB19 values for the Moroccan sample fall within the range of our retrievals, except at 950 nm, where their retrieved k increases against the general decreasing trend. The slope of the dB19 k spectrum is somewhat steeper at shorter wavelengths than in our retrievals.

Several factors may explain differences between our estimates and those of . First, the samples and measurement conditions differ: analysed resuspended dust under controlled chamber conditions, whereas our retrievals are based on ambient, freshly emitted dust measured in the field. Second, the sampled size ranges differ. Our retrieval is based on the PM_2.5 period, whereas used PM₁₀ measurements. This difference can influence the retrieval if particle composition, shape, or density varies with size, even though our inversion assumes size-independent k. Third, in our field setup, the Fidas PSD and the optical measurements were obtained through different sampling lines, which introduces additional uncertainty in the absolute PSD–optical closure. Finally, our procedure applies a consistent Fidas optical-to-geometric diameter conversion for each prescribed particle-shape and refractive-index assumption, whereas used a prescribed refractive-index range for the OPC conversion. The OSSE tests in the Supplement indicate that this latter difference has a relatively small effect when the assumed particle shape is consistent.

A notable difference is observed between the average of the retrievals and the retrieval of the average in panels (b)–(d) of Fig. . The average of the 15 min retrievals is generally larger than the k obtained from the time-averaged dust-event measurements, especially at longer wavelengths. This behaviour is also evident in Fig. S8. We attribute this difference to the disproportionate influence of short periods with lower dust concentration and stronger relative contribution from absorbing non-dust aerosols. When each 15 min interval is weighted equally, these periods can raise the average retrieved k. In contrast, the retrieval of the average is more strongly influenced by high-dust periods, such as the haboob event, which exhibit higher SSA in the near-infrared and infrared and are more representative of mineral dust optical properties.

The violin plots in Fig.  illustrate the sensitivity of k to particle density, ρp, and n. Figure S9 shows the violin plots also for irregular shapes A–F from . Tables  and provide the corresponding numerical values. Table  shows k at selected wavelengths for the three ρp, averaged over all n values. Table  reports k as a function of n, averaged over the different dust particle density assumptions. Both tables include results for a subset of particle shapes; the complete wavelength range is available in Tables S1–S10.

The results show a clear dependence of retrieved k on the assumed particle density. Lower particle densities lead to lower retrieved k, whereas higher particle densities lead to higher retrieved k. This sensitivity arises because particle density affects the aerodynamic-to-geometric conversion used to map the PM_2.5 inlet efficiency onto the dust geometric PSD. As a result, the effective size distribution used in the optical simulations changes with the prescribed density.

The sensitivity to the assumed n is weaker. Table  shows that k decreases slightly with increasing n, with maximum k values occurring at the lower end of the n range. For example, varying n from 1.48 to 1.55 causes changes in k on the order of 0.0002 at 370 nm for irregular particles. This sensitivity is modest compared with the effects of particle density and shape, and values of n for mineral dust are typically confined to a narrower range than would be required to produce large changes in k. This supports the use of fixed, plausible n values rather than attempting a simultaneous retrieval of n and k from the present field dataset.

The third factor explored in Tables  and is particle shape. Particle-shape assumptions exert a substantial influence on the retrieved k. For a given wavelength and set of ρp and n assumptions, non-spherical particles generally require larger k values than spheres to reproduce the observed (tssa). This is particularly evident for triaxial ellipsoids and for the more elongated or irregular MOPSMAP shapes. To quantify the relationship between particle morphology and retrieved k, we use the ratio of volume-equivalent to cross-section-equivalent particle radii, ξvc, from . Lower ξvc values indicate more non-spherical particles. Shapes C, D, and F, as well as the triaxial ellipsoids, have lower ξvc values than spheres, biaxial spheroids, and some of the less elongated irregular shapes, and they yield higher retrieved k. This outcome is consistent with the findings of , who demonstrated through numerical experiments that greater particle non-sphericity leads to higher retrieved k values.

This relationship is further illustrated in Fig. , which shows the retrieved k values as a function of ξvc. A near linear tendency is observed (with increasing k corresponding to decreasing ξvc), i.e. to more non-spherical particles. The slope of this relationship becomes weaker at longer wavelengths. While this relationship appears consistent, it is based on a limited number of data points, and any extrapolation should therefore be treated with caution. Nonetheless, establishing a robust connection between particle non-sphericity and retrieved k could provide a valuable tool for reinterpreting past measurements and datasets – many of which rely on spherical particle assumptions – within the context of more realistic, non-spherical dust models e.g.. For the central assumptions, k retrieved using triaxial ellipsoids can be substantially higher than that retrieved using spherical particles, with the largest relative differences occurring at shorter wavelengths, up to 46 %, and smaller differences at longer wavelengths, up to 10 %.

This apparent relationship between non-sphericity and retrieved k should be interpreted with caution because it is based on a limited set of prescribed shape assumptions. The single irregular shapes are sensitivity cases rather than realistic descriptions of the full atmospheric dust population. Nevertheless, the result demonstrates that particle-shape representation can substantially affect retrieved k. Establishing a more robust link between particle morphology and retrieved absorption properties could help reinterpret past datasets that relied on spherical assumptions and improve the treatment of non-spherical dust in remote-sensing retrievals and climate-model optical-property parametrizations.