The ice nucleation data for mineral dust particles were compiled from 25 published laboratory studies conducted between 2007 and 2023, as summarized in Table S2. These studies were identified through keyword-based searches in Web of Science and Google Scholar. The datasets combined digitized data from figures in literature, open-access repositories, and directly from corresponding authors (see Acknowledgments for details). All data included in our work were published, no unpublished data were considered.

The collected studies focused on the INA of dust particles in the immersion mode with diverse mineral compositions, subjected to varied aging treatments, and generated and measured using both dry-dispersion and wet-suspension methods. Specifically, the dust particles investigated include natural dust from desert and volcanic sources, as well as particles composed mainly of nominally single mineral species, such as illite, quartz, kaolinite, feldspar, and montmorillonite. Ice formation data for Arizona Test Dust (ATD, Powder Technology Inc.) are also included, a formerly widely used surrogate of atmospheric dust in ice nucleation research. Soil dust particles from agricultural sources were excluded, due to their substantially different INA compared to desert and volcanic dust, resulting from their higher organic content and will be addressed in a separate study. In laboratory measurements, dust particles were subjected to various chemical and physical treatments to mimic their atmospheric aging and to evaluate the associated impacts on their INA. These treatments included immersion in water or salt and acid solutions (hereafter referred to as water aging and aqueous aging), thermal treatment, and chemical aging either through exposure to reactive gases or through coating with inorganic and organic acids or secondary organic aerosol (hereafter referred to as gas chemical aging and coating). More details about the aging treatments can be found in Table S2.

Different ice nucleation metrics have been established to quantify and compare INA of dust particles, as summarized by Kanji et al. (2017). In this work, we use ns to compare the INA of different dust types and to develop primary ice nucleation parameterizations. ns indicates the number of ice nucleation events occurring per unit of particle surface. ns is based on the singular description of ice nucleation, which assumes a strong temperature dependence and time is neglected because of the secondary importance of time compared to temperature for dust particles (Welti et al., 2012; Kanji et al., 2013; Budke and Koop, 2015; Wright and Petters, 2013; Vali and Snider, 2015). This choice is motivated by several advantages. ns is an aerosol-aware metric that accounts for particle size and surface characteristics, which aids the mechanistic interpretation of dust INA based on aerosol physicochemical properties. Compared to other available metrics (e.g., nm), ns has the most extensive data coverage among the collected studies, enabling broader and more consistent cross-comparisons. In addition, ns can be readily incorporated into climate models to predict the number of ice crystals induced by dust INPs, if particle size (i.e., surface area) is known. In the present work, the ns are either taken from previous studies or derived based on reported ice nucleation results and particle surface area. While all ns are derived based on the following equations (Eqs. 1–4), the specific equations used differ depending on the measurement methods, i.e., chamber experiments with dry-dispersed aerosol (Eqs. 1 and 2) and DFTs with wet-suspended particles (Eqs. 3 and 4). Table S3 reports for which sources we calculated ns based on the particle surface reported in the source of the study and for which ones the ns was directly reported.

Dry-dispersed aerosol (chamber experiments): 1ns(T)=-ln⁡(1-AF)Sp2AF(T)=Nice crystals(T)Ntotal particles Here, AF (Eq. 2) is the ice activated fraction of aerosol particles, which is calculated based on the number of formed ice crystals (Nice crystals) and total number of aerosol particles (Ntotal particles) measured in the chamber experiments. Sp (Eq. 1) represents the surface area of a single aerosol particle.

Wet-suspended particles (DFT experiments): 3ns(T)=-ln(1-FF)Sd4FF(T)=Nfrozen droplets(T)Ntotal droplets Here, FF (Eq. 4) is the frozen fraction of droplets determined from the number of frozen droplets (Nfrozen droplets) and total number of detected droplets (Ntotal droplets) in the droplet freezing measurements. Sd (Eq. 3) represents the surface area of particles immersed within a droplet.

To account for the stochastic nature (time dependence) of ice nucleation, the time dependent metrics-heterogeneous ice nucleation rate (Jhet, m-2s-1), can be calculated based on following equations (Eqs. 5 and 6). Jhet describes the number of nucleation events per unit particle surface and unit time. Similar to ns, Jhet values are either taken from previous studies or derived from reported ice nucleation results and residence time of aerosol particles or droplets for aerosol (Eq. 5) and suspension measurements (Eq. 6), respectively. Table S3 indicates for which study Jhet was calculated and for which ones the Jhet was directly reported. 5Jhet,dry=ns(T)t6Jhet,wet=-ln⁡(1-ΔNfrozenNunfrozen)sdΔt

In chamber experiments, Jhet,dry is derived based on the residence time (t) of aerosol particles exposed to ice formation conditions (Eq. 5). In DFT measurements (Jhet,wet, Eq. 6), ΔNfrozen denotes the number of droplets that freeze within the time interval Δt from t1 to t2. Nunfrozen is the number of droplets that remain unfrozen at time t1. Δt can be calculated based on the reported cooling rate in each freezing experiment. The residence time of aerosol particles in chamber measurements or the cooling rates in DFTs used in each study are provided in Table S2. We note that the practice of using cooling rate or chamber residence times can underestimate Jhet, because the real time of nucleation could be less than the residence time or the time interval at a given temperature (cooling rate). Since the true ice nucleation time is not possible to observe as we only detect freezing once a crystal has nucleated and grown to detectable sizes, the nucleation rates presented are a lower limit. We further note that compared to ns, Jhet scales the INA of dust particles by the time of an ice formation event. Therefore, if different datasets are measured with identical cooling rates or residence times, the relative ordering of their Jhet values will remain consistent with that of ns.

A compilation of ns values of dust particles with different mineral compositions is shown in Fig. 1. ns values exhibit substantial variability across the studied temperature range (-42 < T < -2 °C), for example, at T = -20 °C, ns spans 7 orders of magnitude ranging from 5.0 × 10³ to 1.7 × 10¹¹ m-2. This variability likely arises from multiple factors, including dust composition, measurement methods and aging processes, we therefore conduct a more detailed investigation into these aspects below.

The ns data were binned into 1 °C intervals at -38 < T < -2 °C and shown as boxplots in Fig. S1 in the Supplement. The median ns values among different dust species differ by 0–6 orders of magnitude at identical temperatures (-38 < T < -2 °C), highlighting the influence of mineral composition on dust INA. Previous studies have reported that feldspar, particularly potassium-rich feldspar (K-feldspar), tends to exhibit higher INA compared to other dust species (Peckhaus et al., 2016; Atkinson et al., 2013; Augustin-Bauditz et al., 2014; Chen et al., 2023b; Harrison et al., 2016). Consistent with these findings, feldspars (plus signs in Fig. 1 and red box in Fig. S1) in the present study generally exhibit the highest ns values at T ≤-2 °C and initiated freezing at the warmest temperature (≈ -2 °C) compared to other dust species. For example, the extremely efficient dust particles at -20 < T < -5 °C and at T < -20 °C were from K-feldspar measured by Peckhaus et al. (2016) and Augustin-Bauditz et al. (2014), respectively. Some ATD (dark blue, triangle) (Kanji et al., 2013; Perkins et al., 2019) and natural volcanic dust (green, squares with cross) measured by Fahy et al. (2022) also display relatively high ns values at warm freezing temperature (≈ -2 °C). Such high INA of ATD and volcanic dust was attributed to the presence of K-feldspar in these particles, as supported by mineral and chemical characterizations of ATD (Chen et al., 2023b; Broadley et al., 2012) and volcanic dust (Fahy et al., 2022). The ns boxplots of different dust species (Fig. S1) show a similar trend, with K-feldspar exhibiting the highest INA followed by ATD. However, not all feldspars are as ice-active as K-feldspar. As shown in Figs. 1 and S1, non-potassium-rich feldspars can exhibit ns values as low as those of quartz and illite (Harrison et al., 2016) at -21 < T < -5 °C. Similar variability of ns is also observed for other dust species, including illite, ATD and quartz (Figs. 1 and S1). This variability can be partly explained by the inherent complexity of chemical composition, crystal structure and surface properties of dust, even for particles within the same mineral group. For example, several studies (Peckhaus et al., 2016; Harrison et al., 2016) found that K-feldspar exhibits higher INA than calcium-rich feldspars. The high INA of K-feldspar has been linked to surface defects with a particular crystallographic face (Kiselev et al., 2017) and to the kosmotropic (structure making) characteristic of K+ in the surface layer (Zolles et al., 2015). Although Kumar et al. (2018) indicate that the high INA of K-feldspar may not be due to the presence of K+, they observed decreased INA of K-feldspar when more K+-containing solutes are added to dust suspensions. Whale et al. (2017) examined different types of feldspars and showed that the exceptional INA of alkali feldspars arises from specific topographical features: the perthitic microtexture resulting from phase separation into Na- and K-rich regions. This would explain the variability in ice nucleation across the feldspar group, with the Na/K feldspars showing superior INA than other feldspars which lack such microtextural features. Harrison et al. (2019) measured the INA of 10 types of α-quartz samples and found that ns varied 1–2 orders of magnitude at T ≈ -13 °C.

In addition to the compositional diversity of dust particles, the variability in ns can also arise from differences in measurement methods, as suggested by Hiranuma et al. (2015) and Emersic et al. (2015). Accordingly, we divided the ns values of each dust component into two groups: dry-dispersed aerosol (aerosol, hollow circles) and wet-suspended particles (suspension, solid circles) and shown in Fig. 2. The datasets are categorized into seven mineralogical species, including K-feldspar, other non-potassium-rich feldspars, quartz, illite, kaolinite, montmorillonite and ATD. Across most mineralogical groups, differences between aerosol and suspension ns values at overlapping temperatures are observed. For example, aerosol ns of K-feldspar is up to 2 orders of magnitude higher than suspension ns at T = -20 °C.

Figure S2 presents boxplots of ns separated by methods for different dust compositions. The median ns values between aerosol and suspension differ by 1–5 orders of magnitude at overlapping temperatures (Fig. S2). Specifically, the median ns values between aerosol and suspension methods differ by up to 3 orders of magnitude at -31 < T < -18 °C for illite, by 3–5 orders of magnitude at -27 < T < -21 °C for ATD and up to 1 order of magnitude at -35 < T < -30 °C for kaolinite. K-feldspar and kaolinite nsshow the closest agreement between the two methods, with differences generally within an order of magnitude. This comparison cannot be made for quartz and other non-potassium-rich feldspars because of the lack of aerosol ns data for these dust species. We also acknowledge that these method-based comparisons within individual dust groups are still subject to uncertainties arising from compositional differences within individual dust categories. For example, the composition of ATD is known to vary with particle size (Vlasenko et al., 2005), which may influence its INA. Within the non–potassium-rich feldspar group, albite (Na-end member) exhibits a higher ns compared to anorthite (the calcium-end member) (Harrison et al., 2016). Nevertheless, the consistent differences observed across multiple groups suggest that measurement method contributing to the variability in reported dust INA cannot be excluded but this is likely convoluted by changes in composition.

Figure 3a compiles data from different compositions, showing that aerosol measurements (red) generally yield larger ns values compared to suspension measurements (blue). The corresponding ns boxplots for the combined data are shown in Fig. S3a. The median values of aerosol ns are up to 6 orders of magnitude higher than suspension ns at -38 < T < -18 °C, with the ns gap narrowing with decreasing temperatures. These findings confirm that measurement method is a large source of ns variability and that the magnitude of this effect depends on dust composition. In addition, wet suspension ns exhibits two distinct regimes of increases (Figs. 3a and S3a), with a steeper temperature dependence observed at -38 < T < -18 °C compared to -18 < T < -3 °C.

To account for the time-dependent nature of ice nucleation, the Jhet was estimated using Eqs. (5) and (6) and shown in Fig. 3b. The corresponding Jhet boxplots are shown in Fig. S3b. Similar to ns, aerosol Jhet values are systematically higher than suspension Jhet values (Fig. 3b), with differences in median Jhet values up to 6 orders of magnitude at -37 < T < -19 °C (Fig. S3b). The gap between aerosol and suspension Jhet narrows with decreasing temperature and becomes comparable (within 1 order of magnitude) at T < -27 °C. Similar to ns, suspension Jhet also shows a stronger temperature dependence at -42 < T < -18 °C while remain relatively constant at T > -18 °C. The two-step temperature dependence of both ns and Jhet underscores the necessity of explicitly incorporating this feature into ice formation parameterizations. Neglecting this feature may lead to an underestimation of dust INA at colder temperatures (T < -18 °C) and overestimation of ns and Jhet at warmer temperatures (T > -18 °C).

The difference between aerosol and suspension ns and Jhet may be attributed to differences in water-particle interactions between two methods (Emersic et al., 2015; Hiranuma et al., 2015). In DFTs (suspension), particles are suspended in water droplets prior to freezing measurements, whereas in chamber experiments (aerosol), particles activate into cloud droplets in situ within a few seconds. This different timescale of water-particle contact can cause varying degrees of particle surface alteration, thereby influencing their INA (Hiranuma et al., 2015). This process, known as water aging (Harrison et al., 2019; Kumar et al., 2019a), its impact on dust INA is further explored in Sect. 3.3. Another potential factor may be the difference in particle concentrations within droplets generated by the two methods. Some suspension samples in DFTs may be prepared at higher particle concentrations, whereas chamber measurements detect freezing of droplets activated from individual particles. As noted by Emersic et al. (2015), high particle concentrations may cause coagulation either within suspensions or within the formed droplets, reducing the effective surface area available for nucleation and thereby lowering INA.

We calculated the spherical equivalent diameter (De) of dust particles contained in a droplet for both measurement methods. The diameter of the spherical equivalent particle is calculated based on De=Sp or Sdπ, where the Sp is the surface area of an individual aerosol particle in aerosol measurement, and Sd is the total surface area of suspended particles in a droplet in suspension measurement. The De method treats the combined surface area of multiple particles as that of a single INP. The underlying assumption is that a multi-particle droplet (those measured by DFT) can be conceptually dispersed into many smaller, single-particle droplets. Given that the freezing of a droplet is triggered by the most active ice-nucleating site on particle surface and the probability of encountering such an active site scales with the total particle surface area, the De method normalizes a muti-particle droplet into an equivalent single INP that shares the same ice-nucleating probability. This approach enables a direct comparison among different ice nucleation measurements. The relationship between De and droplet freezing temperatures (T) is shown in Fig. 4, where the freezing temperature corresponds to the point at which 50 % of droplets (FF = 50 %) are frozen in the suspension measurements, and at which 10 % of particles (AF = 10 %) are ice-activated in the aerosol measurements. The freezing temperatures at FF = 50 % and AF = 10 % are the representative freezing thresholds for suspension and aerosol measurements, respectively. While a 50 % FF captures the median freezing behavior of a droplet population where aerosol particles are aggregated in individual droplets, achieving a 50 % AF in aerosol measurements is often impractical due to the measurement on single sub-micron aerosol particles in droplets and therefore the inherent rarity of INPs. The lower INA of some aerosol species in a single droplet leads to lower detected INA. Consequently, a 10 % AF is adopted as a widely reported, statistically significant threshold for evaluating the ice-active state of aerosols in chamber experiments. We note that the selection of different frozen or active fraction threshold may lead to a shift in the threshold freezing temperature.

The freezing temperature of dust particles increases with De, with 80 % of suspension measurements showing De values about 1 order of magnitude larger than aerosol measurements at identical freezing temperatures (Fig. 4a). For example, suspension De values (≈ 10–7000 µm) are 1–3 orders of magnitude larger than those from aerosol measurements at T = -25 °C. Particles with different mineral compositions indicate a similar trend (Fig. 4b), with larger suspension De values. This explains why suspension droplet experiments freeze at warmer temperatures than droplets activated from individual aerosol particles (Fig. 4), as a greater particle surface area increases the probability of ice formation. At high particle concentrations in suspension measurements, coagulation may occur which cannot be fully verified in this study. Should it occur, coagulation could reduce the effective surface area available for ice nucleation but would only be relevant if water were unable to access the aggregated regions of the dust particles. Since ns is obtained by normalizing freezing ability of droplets with the suspension BET surface area, the calculated ns may be lower than those that would be obtained in the absence of coagulation (i.e., a case similar to aerosol measurement with individual particles).

In this work, it is not possible to provide direct observational evidence for particle coagulation or its impact on INA of dust particles. Nevertheless, we noted that there are few suspension measurements with De (≈ 1 µm) at -38 < T < -22 °C that also occur for aerosol measurements (Fig. 4), suggesting that agreement between the two methods is observed at sufficiently low particle suspension concentrations. These data correspond to suspension measurements with picoliter-size droplets (compared to microliter-droplet measurements) at warmer temperatures and therefore result in dust equivalent particle sizes (≈ 1 µm) that are more atmospherically relevant. Simulations by Emersic et al. (2015) suggest that picoliter-size droplets are unlikely to contain multiple particles, reducing the likelihood of significant coagulation effects compared to microliter-sized droplets. Consequently, the freezing behavior of picoliter-size droplets is more comparable to droplets activated from single particles in aerosol measurements. Many DFT measurements employ serial dilution to probe suspensions with different INP concentration ranges and overlapping INP concentrations are often observed across the dilution series. These dilution results combined with our De data (Fig. 4), suggest that particle coagulation is not expected in all suspension experiments, but could not be excluded as a possibility under conditions of high particle concentrations.

This interpretation is also consistent with Figs. 3 and S3, where the ns and Jhet difference between two methods decreases with decreasing temperatures. This is because DFT measurements at relatively lower temperatures (-38 < T < -19 °C) were conducted using picoliter-size droplets (Table S2) (Atkinson et al., 2013; Hiranuma et al., 2015; Zolles et al., 2015) with low particle concentration and minimal potential for particle coagulation. These findings suggest that coagulation effect may be one possible factor contributing to the different ns values obtained between two methods, especially that observed for relatively warm temperatures.

The findings presented in Sect. 3.1 and the current section highlight that both chemical composition and measurement methods are important factors causing the observed variability in the INA of dust particles. Building upon these findings, we develop specific parameterizations in the following sections to systematically represent the INA of dust particles. We fitted the ns and Jhet temperature dependent parameterizations using a log-linear form: 7log(ns)=A×T+B

This log-linear form is an empirical parameterization chosen to summarize the compiled datasets, capturing the general trend of temperature dependence of ns and Jhet. Alternative fitting approaches, including polynomial and exponential forms, were also tested but did not improve the representation of the data. The fitted parameters (A,B) and coefficients of determination (R2) for different measurement methods are indicated in Fig. 3 and summarized in Table S4. For example, aerosol ns shows a weak correlation with temperatures at -41 < T < -13 °C, expressed as follows. To represent the INA of dust aerosol across the entire MPC temperature range, this fitted relationship was extrapolated to T = -2 °C (Fig. 3a). 8log(ns)=-0.12081T+7.18228(R2=0.30)

As discussed above, suspension ns exhibits two-step temperature dependence, with a stronger temperature dependence at -41 < T < -18 °C and a weaker dependence at -18 < T < -2 °C. Accordingly, the suspension ns data were fitted separately in two temperature regimes. The resulting parameterizations are summarized below:

-41 < T < -18 °C: 9log(ns)=-0.33986T-0.85008(R2=0.50)

-18 < T < -2 °C: 10log(ns)=-0.08606T+3.65451(R2=0.12)

The prediction band at the 95 % confidence level (shaded area in Fig. 3) captures most data points (Fig. 3a), indicating the strong predictive capability of the developed ns parameterizations. Specifically, 4449 out of 4661 and 5062 out of 5321 ns values for suspension measurements at T < -18 °C and T > -18 °C were captured, and 1462 out of 1535 for aerosol measurements were captured. Similar parameterizations were developed for Jhet with the resulting functions shown in Fig. 3b.

Given the distinct INA among dust particles with different compositions, we also developed ns parameterizations for individual dust species (K-feldspar, non-potassium feldspar, quartz, illite, kaolinite, montmorillonite and ATD), as indicated by the log-linear fits in Fig. 2 and summarized in Table S4. These composition-specific parameterizations (Fig. 2 and Table S4) are intended for estimating atmospheric INP contributions from individual mineral types. They are therefore suitable in regions where a single mineral dominates the dust population, if its local abundances are known. In contrast, the parameterizations developed from the dataset of all dust types (Fig. 3) represent the average freezing behavior of dust particles with mixed mineral compositions, which are suitable for predicting INPs in an atmosphere with compositionally diverse dust or for global-average applications. Applying ns-based parameterizations for aerosol and suspension requires distinguishing whether dust particles have been influenced by water exposure, necessitating additional physicochemical characterization of particle surfaces. To address this limitation, an De-based parameterization was developed by fitting De as a function of droplet freezing temperature:

-38 < T < -3 °C: 11T=4.28472×log(De)-42.37274(R2=0.73)

As shown in Fig. 4a, this De parameterization is independent of measurement method and predicts the freezing temperature of droplets directly from the surface area of immersed particles. Although this parameterization applies to a wide particle size range up to 7000 µm under specific experimental conditions (Fig. 4), it fully covers particle sizes relevant for atmospheric dust (De < 10 µm). It is applicable across the MPC temperature regime and can be readily used whenever particle size information is available. The 95 % prediction band (shaded area in Fig. 4) demonstrates the strong predictive capability of the developed De parameterization, with 120 out of 122 data points successfully captured. We also note the variability in freezing temperature observed at similar De values (Fig. 4), indicating that other factors other than De, including droplet volume, dust mineral composition and other experimental conditions, may also influence droplet freezing.

The INA changes resulting from various aging processes are also investigated. Experimental details of aging treatments are provided in Table S2. Figure 5 shows ns data for fresh (solid colors) and aged (faded colors) dust particles, with dry aerosol (Fig. 5a) and wet suspension (Fig. 5b) shown separately. For both aerosol and suspension, fresh (solid colors) and aged particles (faded colors) exhibit differences in ns at identical temperatures. This indicates that atmospheric aging modifies the INA of dust particles. To quantify these differences, ns data were binned into 1 °C intervals and are shown in Fig. S4. For aerosol measurements (Fig. S4a), the median ns values of fresh (solid color) and aged (faded color) particles differ by 1–2 orders of magnitude at -41 < T < -20 °C. A similar difference is observed in fresh and aged suspension ns at -27 < T < -3 °C (Fig. S4b). These differences are notably smaller than those caused by measurement methods. For example, the difference between ns from dry-dispersion and wet-suspension can reach up to 6 orders of magnitude in the same temperature range (Figs. 3 and S3). To statistically evaluate the impact of aging on the INA of dust particles, significance analyses were performed for both aerosol (-41 < T < -20 °C) and suspension measurements (-27 < T < -3 °C). The results yielded statistically significant differences (p < 0.05) between fresh and aged samples in both measurement methods, indicating that the aging process alters the INA of dust particles. The details about the significance analyses are provided in Tables S5 and S6 and Text S1 in the Supplement.

In addition, ns of both fresh and aged particles from suspension measurements (Fig. S4b) exhibit a stronger temperature dependence at lower temperature (-38 < T < -18 °C) and a weaker dependence at higher temperature (-18 < T < -3 °C). This feature aligns with the observation when fresh and aged datasets are combined (Fig. S3). Therefore, ns parameterizations for fresh and aged particles are developed by explicitly considering their temperature dependence, as shown in Fig. 5. For both fresh and aged aerosol measurements, the ns parameterizations are extrapolated to T = -2 °C (Fig. 5a) to represent the INA of dust particles that have experienced minimal interaction with water across the entire MPC temperature range.

Based on Figs. 5 and S4, it is challenging to draw a conclusion on how the aging process will impact the INA of dust particles, since aging does not consistently increase or decrease ns at different temperatures. The direction and magnitude of the aging effect may depend on the specific dust species and the aging pathways. To account for this, Fig. S5 provides elaborated ns boxplots for individual dust species, including K-feldspar, non-potassium-rich feldspars, quartz, ATD and natural dust (separated desert and volcanic dust). Clay particles (illite, kaolinite, and montmorillonite) are not shown due to limited data from two methods for comparison between fresh and aged particles. Given the significant contribution of clay particles to dust mass (Atkinson et al., 2013), additional measurements are needed to quantify the aging effect on their INA. Figure S5 indicates that aging may have different effects on INA of different dust species. Specifically, ATD and natural dust (both desert and volcanic dust) show a decrease of 1–2 orders of magnitude in ns at -39 < T < -3 °C after aging, with the effect becoming more pronounced at lower temperatures. In contrast, the ns changes of K-feldspar and quartz particles are generally within 1 order of magnitude, indicating that these dust species are relatively insensitive to aging. This observation is consistent with Harrison et al. (2016), who found most K-feldspars exhibited no significant change in INA after water aging. While Harrison et al. (2019) noted that certain quartz can be highly sensitive to water aging, only one of the three samples exhibited a pronounced decrease in ns after 10 months of water aging, whereas ns decrease of other quartz samples were within 1 order of magnitude. Their findings therefore align with our results showing insensitivity of quartz to aging treatments. The response of non-potassium-rich feldspar (“other feldspars” in Fig. S5) (Harrison et al., 2019; Peckhaus et al., 2016) to aging process, including albite and anorthite, is more variable. Depending on temperature, aging can increase, decrease or leave their ns unchanged (Fig. S5). For most temperatures, the ns changes of other feldspars remain within 1 order of magnitude. An exception occurs at -25 < T < -23 °C, where the ns of aged particles increased by 2–5 orders of magnitude compared to fresh particles. This occurs because most fresh non-potassium-rich feldspars exhibit relatively low INA. However, one aged sample from Harrison et al. (2016) displayed markedly higher INA, which strongly influenced the median ns values. This variability reflects both the compositional diversity of feldspars and the limited number of available datasets for aged non-potassium-rich feldspars, as indicated in Fig. S5. Overall, our results suggest that feldspars (both K-feldspar and other feldspars) and quartz are generally insensitive to aging process compared to the natural dust and ATD samples presented here.

We further investigate how different aging pathways influence the dust INA and develop corresponding ns parameterizations. As mentioned in the methodology, laboratory aging treatments (Table S2) are categorized into four groups: water aging, aqueous aging (salt and acid solutions), gas chemical and coating, and thermal treatments. The resulting ns boxplots of dust particles subjected to these aging processes are presented in Fig. 6. It should be noted that only studies reported paired measurements of fresh and aged ns for the same dust type are considered. This restriction is necessary because ns values vary strongly among mineral species, and some species may have measurements only for the fresh state. Including such incomplete datasets could bias the median ns of fresh dust. For example, if the relatively low ns of fresh quartz (Fig. 1) was included and compared to aged feldspar ns with exceptionally high INA (Fig. 1), the overall median ns for fresh dust would be artificially lowered, confounding the assessment of aging effects. A similar artifact was observed in the case of non-potassium-rich feldspars (Fig. S5) at -25 < T < -23 °C, where the median ns of aged particles appears higher than fresh particles, due to the inclusion of datasets lacking paired measurements. For reference, the ns derived from the complete datasets (without this restriction) is presented in Fig. S6. As expected, ns values of fresh dust overlap with those from different aging processes, making it difficult to isolate the impact of each aging process (Fig. S6). This is because the larger number of the fresh ns datasets compared to aged ns and their strong variability caused by mineral compositions. To minimize this bias, we therefore focus on paired datasets of fresh and aged ns and develop parameterizations accordingly (Fig. 6).

As shown in Fig. 6, aging pathways exert variable impacts on the INA of dust particles from both aerosol (open box) and suspension (solid box) measurements at -40 < T < -2 °C. The aerosol and suspension ns across different aging pathways vary by 1–2 and 1–4 orders of magnitude at -40 < T < -20 °C and -26 < T < -2 °C, respectively. By fitting the median ns values at different temperatures, we find that aged particles subjected to all aging pathways (both aerosol and suspension) generally exhibit lower INA than fresh particles (red curves). This aligns with most studies (Table S2), which report reduced INA after various aging treatments.

For suspension measurements, aging via chemical reactions and coating causes the most significant decrease in dust INA (purple curves), followed by thermal treatment (orange curves). Aqueous aging of dust particles in water (light blue curves) or salt/acid solutions (dark blue curves) also decreases INA of dust particles. The impacts from aqueous and water aging are similar but weaker than those induced by chemical and thermal treatment. This trend is also supported by a case study of ATD, the only dust species for which INA has been reported under all aging treatments. Figure S7 explicitly shows the ns boxplots of fresh and aged ATD. Specifically, suspension measurements of ATD show the largest decrease in ns under gas chemical and coating aging, followed by thermal treatment. The impact of water and aqueous aging are similar but less significant compared to the other two aging processes. The results from ATD confirm that the general conclusion drawn from selective studies (Fig. 6) is representative of the aging impact on individual dust species. Given the different magnitudes of aging effects, we developed separate ns parameterizations for dust particles subjected to distinct aging processes, as listed in Fig. 6. However, to represent the INA of bulk fresh and aged particles, we would recommend using parameterizations listed in Fig. 5, which better capture the average INA of dust particles with diverse compositions.

These findings regarding water/aqueous aging are consistent with previous studies, which report that immersion in solutions generally lowers the INA of dust particles. For example, Reischel and Vali (1975) reported suppression of freezing in kaolinite-containing particles by NaI, KI, NaCl, CsCl and K2SO4. Abdelmonem et al. (2017) observed a reduction in the INA of α-alumina immersed in solutions of various pHs. Exceptions occur for ammonia or ammonium salts, where increases in INA have been observed for quartz (Whale et al., 2018), kaolinite (Reischel and Vali, 1975; Kumar et al., 2019b) and feldspars (Klumpp et al., 2022; Whale, 2022; Whale et al., 2018). Whale (2022) attributed this enhancement to the disruption of thermodynamically unfavorable hydrogen ordering in ice clusters induced by water-orienting nucleating surfaces (i.e., dust surfaces) in NH4+-containing solutions. In contrast, Kumar et al. (2019b) hypothesized that the adsorption of NH3 and NH4+ on the feldspar and kaolinite promotes the arrangement of water molecules into an ice-like pattern through hydrogen bonding, and thereby enhancing the ice nucleation of dust particles. These exceptions highlight the variability in the freezing behavior of different dust species in response to diverse aging processes (Fig. 6). Therefore, the fitting curves in Fig. 6 represent the average INA of aged dust particles with mixed mineral compositions and interact with various solutes. We note that the actual dust INA may deviate from these average trends in specific atmospheric environments where certain reactive solutes (e.g., ammonium salts) dominate the aging pathways. A reduction in INA after aging has also been observed in natural dust samples (Fig. S5), indicating that this general aging effect is representative of atmospheric dust behavior. We also note that data for water and aqueous aging of dry-dispersed dust particles (aerosol) are very limited. This makes the evaluation of water and aqueous aging impact on dust particles from dry dispersion not feasible, we thereby encourage further studies on this aspect. Nevertheless, the finding that water/aqueous aging generally reduces dust INA, may help explain the difference in ns between dust particles generated by dry dispersion and wet suspension (Sect. 3.2). Because dust particles in suspensions remain in contact with water for longer periods during sample preparation, they may exhibit lower ns values relative to dry dispersed particles. We therefore emphasize that water should be considered a reactive medium in ice nucleation studies. In particular, the preparation and storage duration of dust suspensions may introduce uncertainties in quantifying the INA of dust particles and should be carefully controlled.