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Abstract
We present the characterization of several atmospheric aerosol analogs in a tabletop chamber and an analysis of how the concentration of NaCl present in these aerosols influences their bulk optical properties. Atmospheric aerosols (e.g., fog and haze) degrade optical signal via light–aerosol interactions causing scattering and absorption, which can be described by Mie theory. This attenuation is a function of the size distribution and number concentration of droplets in the light path. These properties are influenced by ambient conditions and the droplet’s composition, as described by Köhler theory. It is therefore possible to tune the wavelength-dependent bulk optical properties of an aerosol by controlling droplet composition. We present experimentation wherein we generated multiple microphysically and optically distinct atmospheric aerosol analogs using salt water solutions with varying concentrations of NaCl. The results demonstrate that changing the NaCl concentration has a clear and predictable impact on the microphysical and optical properties of the aerosol.
© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. INTRODUCTION
Fog, mist, and haze are classes of polydisperse, naturally occurring atmospheric aerosols that form in every region of the world [1]. These aerosols are not only distinct from one another, but also experience significant case-by-case variation within the same class [1–4]. This variation is due, in part, to the availability of viable condensation nuclei in the ambient environment [1,5]. These nuclei are often small (diameter ${\lt}\;{200}\;{\rm nm}$) water-soluble particles, such as salts, which allow for droplet formation through heterogeneous nucleation [1,5]. In addition to ambient environmental conditions (temperature, pressure, etc.), the composition and concentration of these water-solubles nuclei control the ultimate diameter of nucleating droplets at thermodynamic equilibrium [1,5]. In real-world environments, the amount and type of solutes present in the atmosphere vary but often include salts (especially near sources of salt water), lead compounds, sulfur oxides, carbon oxides, and nitrogen oxides [6].
Once formed, atmospheric aerosols form degraded visual environments (DVEs), degrading optical signals via scattering and absorption of light, as can be seen in Fig. 1. This is detrimental to systems that rely on clear optical signal, including remote sensing, optical telecommunications, and transportation [7,8]. Application of Mie scattering theory and the Beer–Lambert law (also called Beer’s law or the Bouguer–Lambert law) can provide insight into signal degradation within DVEs, and there has been significant previous work performed modeling these processes [9–13]. However, due to the variable nature of atmospheric aerosols, the type and degree of signal degradation is similarly varied on a case-by-case basis, creating a complex problem space that requires additional experimentation to better inform models and optical sensing system design.
Fig. 1. Scattering of multiple visible spectrum (red: 670 nm, green: 532 nm, and blue: 480 nm) collimated beams in an experimentally generated atmospheric aerosol. All beams become isotropic by the midpoint of the chamber due to scattering.
There has been significant research performed on separately optically and microphysically characterizing aerosols; however, much of this research has focused on only one characterization or the other. State-of-the-art study of aerosol formation and evolution is performed using sophisticated systems that can control for the many factors that influence aerosol microphysics [14,15]. Though very effective, developing and operating such systems requires a significant amount of expertise and expense. As such, optical experiments in the literature often rely on aerosol generation systems that offer minimal, if any, control over the properties of the resultant aerosols, such as readily available ultrasonic humidifiers. Because of this, optical-aerosol experimentation is often performed on aerosols that the experimenters have no control over and are often not directly relevant to real-world conditions.
As such, there remains a need for experimentation that generates atmospherically relevant aerosols while concurrently characterizing both their microphysical and bulk optical properties. To address this, we present a simple technique for generating tunable, atmospherically relevant aerosols for optical experiments using commercially available components. This research is necessary to bridging the gaps that exist between extant optical and microphysical aerosol research while leveraging the significant contributions made to the literature by each of these disciplines. To this end, we have developed a novel tabletop chamber to generate and characterize the optical and microphysical properties of bulk aerosols. This chamber is designed to be constructed from economical, commercially available components and simple to operate. We show that it is possible to tune the microphysical, and thereby optical, properties of aerosols generated in this chamber by introducing and varying the concentration of solutes to the feed water used to generate the aerosols. In doing so, we additionally show that this system is capable of generating aerosols that are analogous to fogs observed in real-world data collections. For this work, we focus on sodium chloride (NaCl) salt as our solute of interest, as it is both atmospherically relevant and well modeled in the literature [16–19]. We present initial experimentation generating and characterizing a number of atmospherically relevant aerosols, linking initial solute concentration to bulk optical transmissivity.
2. BACKGROUND AND THEORY
We show that solute concentration influences the wavelength-dependent bulk optical properties of an aerosol by changing the droplet diameter distribution of the aerosol. The effect of solute concentration on the diameter of individual droplets can be described through Köhler theory, detailed in Section 2.A. The wavelength-dependent scattering effect of a single droplet can be described by Mie scattering theory (Section 2.B). Finally, the Beer–Lambert law provides a method to integrate the scattering effects of many individual droplets to solve for a homogenized attenuation coefficient within the aerosol (Section 2.C).
A. Droplet Nucleation
Atmospheric aerosols exchange water vapor between droplets and the environment via nucleation. This process is described by Köhler theory, which computes the thermodynamic equilibrium of a droplet and the environment [20]. The Köhler equation for a droplet can be written as [21,22]
where $S$ is the ratio between the partial pressures of water in the ambient environment and at the surface of the droplet, referred to as the saturation ratio, $d$ is the droplet diameter in m, and ${m_s}$ is the mass of solute present within the droplet in kg. The left-hand term within the exponential ($\frac{A}{d}$) is referred to as the curvature effect, where $A$ captures the effects of ambient conditions and surface tension on the size of the droplet. The right-hand term within the exponential ($B\frac{{{m_s}}}{{{d^3}}}$) is referred to as the solute effect, where $B$ describes the dissociation of the solute within the droplet.In physical terms, this equation defines the thermodynamic balance between the curvature and solute effects as a function of environmental conditions and droplet size and composition. The curvature effect (driven by surface tension) promotes evaporation of water from the droplet, while the solute effect (driven by chemical potential) promotes absorption of water into the droplet. As the amount of solute within the droplet increases, so too does the influence of the solute effect on droplet size, increasing the size [1,22,23]. This formulation is particularly useful for the discussions in this paper as it shows the direct relationship among ambient conditions, droplet size, and the concentration of solute within the droplet. Further explanation and analysis of Köhler theory can additionally be found in Stull [1], Seinfeld and Pandis [24], and Köhler [20].
B. Single Droplet Scattering
Due to the effects of surface tension, water droplets suspended in air are approximately spherical. This approximation, and the fact that the spheres are far apart from one another, allows the application of Mie scattering theory. Mie scattering theory describes the interaction between an electromagnetic plane wave and a homogeneous dielectric sphere, to model the scattering and absorptions of individual droplets [10,25]. Mie theory calculates scattering and absorption cross sections as a function of wavelength, droplet diameter, and refractive index (RI). We employ a MATLAB code from [26] to perform these calculations.
Mie theory accounts for both scattering and absorption; however, we have previously shown that descriptions of light transport in aerosols can neglect absorption for wavelengths where the scattering cross section is more than an order of magnitude larger than the absorption cross section [27]. This approximation is reinforced by comparing the real and imaginary RIs of water at the desired wavelengths [27–29]. For wavelengths where the real RI is more than an order of magnitude greater than the imaginary RI, the effects of absorption can be neglected [27]. For this experiment, we use a 532 nm laser and by the diffusion approximation, disregard the absorptive effects of the aerosols from our analysis.
C. Bulk Aerosol Scattering
The homogenized or bulk scattering effect of many droplets can be calculated by taking a weighted sum of the impact of individual droplets [10]. This is done by weighting the scattering effects of all droplets with the same diameter by the relative volume fraction of those droplets in the environment. This calculation can be formulated as [27]
where ${\mu _s}$ is the scattering coefficient of the bulk aerosol in ${{\rm m}^{- 1}}$, $N$ is the overall number density of aerosolized droplets in the ambient environment (in ${{\rm cm}^{- 3}}$), ${\sigma _{{s_i}}}$ is the scattering cross section in ${{\rm m}^2}$ of a droplet with diameter ${d_i}$ for light with wavelength $\lambda$, and ${n_i}$ is the relative number fraction of droplets with diameter ${d_i}$ within the aerosol. The scattering coefficient, ${\mu _s}$, gives a measure of how much incident light is scattered per unit length of the aerosol. For the case of negligible absorption, ${\mu _s}$ is identical to the extinction coefficient in the Beer–Lambert law [9,27,30].Finally, it is useful to calculate the meteorological optical range (MOR) of light within the aerosol. MOR is defined as the distance required to reduce the flux of a collimated beam to 5% of its initial value [31]. MOR (in m) can be calculated using
MOR is a valuable metric for understanding the effect of an aerosol on optical systems, and is preferable to qualitative metrics such as visibility, as MOR is quantitative in nature and can be easily related to other radiometric quantities.D. Light Attenuation as a Function of Droplet Composition
Through analysis of Eqs. (1)–(3), it is clear that the optical scattering properties of an aerosol can be changed by modulating the solute mass within individual aerosols. This phenomenon is expected, as increasing the amount of solute present within a single droplet should increase its diameter [1,5,20]. By increasing the overall solute concentration in the feed water used to generate an aerosol, we show an increase in the overall mean diameter of the generated droplets (as discussed in Section 2.A). As the mean diameter of the aerosol changes, we anticipate a wavelength-dependent change in the optical properties of the aerosol as described by Mie scattering theory. We show that this relationship allows us to tune the optical properties of an aerosol by controlling the solute concentration present in the feed water used to generate the aerosol.
3. EXPERIMENTAL SETUP
We developed a novel tabletop chamber to act as an optical test-bed capable of concurrent aerosol generation and characterization. Aerosols are generated in the chamber using a compressed flow connected to a shearing spray nozzle. We then characterize the aerosols using an in-house transmissometer [23,27,30,32–34], a Malvern Panalytical droplet sizer, and multiple Omega Engineering temperature and RH probes to track ambient conditions.
A. Tabletop Aerosol Chamber
Aerosols are generated in a rectangular chamber with dimensions ${120} \times {45} \times {38}\;{\rm cm}$, depicted in Fig. 2. The chamber has an additional 38 cm tall, trapezoidal head space allotted for aerosol generation and mixing. A 1/4 J air atomizing spray nozzle from Spraying Systems Co. is placed in the center of the top panel and is connected to an air compressor and a water tank via non-static tubing. The nozzle operates by atomizing a water flow from the tank with a shearing air flow, spraying aerosolized water droplets into the chamber. Both head pressures for the water tank and the shearing airflow are generated by the compressor, which is operated at 500 kPa. To replicate the results of the presented methodology we recommend the use of the same spray nozzle and operating pressure; however, similar results should be obtainable with other commercially available aerosol generation systems, such as the Topas ATM 222. The chamber is constructed from 6 mm thick acrylic paneling and 2.5 cm aluminum t-slots, sealed with silicone epoxy at the connections and clamped along the lid line to ensure no leaks occur during experimentation.
Fig. 2. Sandia National Laboratories aerosol test-bed shown (a) as an initial schematic and (b) assembled and operating at a test site.
The chamber is equipped with six optical paths (five across the long axis of the chamber and one across the short axis), each fit with hydrophobic optical windows that are transparent in the visible wave band (stock number 34-528 from Edmund). In addition to the optical paths, the chamber is fit with multiple Swagelok bulkhead connections (SS-600-11-6 from Swagelok) to allow for many different configurations of equipment to support testing needs. Figure 2 shows one such possible configuration of the test chamber, where several of the longer optical paths are utilized for aerosol characterization tests.
Table 1. Solute Concentrations Used in Experimentation
B. Variation of Solute Concentration
We tune the optical properties of the generated aerosols by changing the concentration of solute (NaCl salt) present in the feed water. We focus our efforts on NaCl salt due to its presence in the literature and observed in real-world aerosols [2,4,13,16–19]. Aerosols with five different concentrations of NaCl were generated during experiments, as summarized in Table 1. For convenience, we label each concentration as Aerosols 1 through 5, as each concentration generates a difference aerosol. We tested aerosols with a NaCl concentration of up to 35 g/L, as that is the average salinity of sea water [35,36]. In future work, we will analyze the effects of higher concentrations on the size distribution.
We use distilled water from a laboratory source and ${\ge} 99\%$ NaCl salt from Sigma.
C. Characterization Equipment
The equipment used to characterize the aerosols has been described in detail in [23,27,30,32–34]. Briefly, we measure droplet size distribution with a Malvern Panalytical Spraytec, a diffraction-based particle sizer that connects to the chamber via an inhalation cell. We measure optical transmission with an in-house transmissometer aligned across the long path length (120 cm) of the chamber (as seen in Fig. 2). The transmissometer measures optical transmission at 532 nm and has a dynamic range of seven orders of magnitude. We monitor temperature and RH within the chamber using a pair of Omega SP-004-2 probes.
D. Detailed Procedure
Prior to generating an experimental aerosol, a “humidification spray” is performed, wherein distilled water is flowed through the spray nozzle at 500 kPa for 10 min. The purpose of this spray was to raise the RH within the chamber back up to saturation (100%) to extend the life of the experimental aerosols (without this step, experimental aerosols dissipated over the course of a couple minutes). Once the aerosol generated by the humidification spray had fully dissipated, as indicated by both the Malvern and transmissometer, the experimental aerosol was then generated. This was done by flowing distilled water mixed with the appropriate amount of salt (see Table 1) through the spray nozzle at 500 kPa until optical transmission through the chamber stabilized at a minimum value, which took 5 min. The generation system was then turned off and the aerosol allowed to evolve and dissipate until conditions within the chamber returned to baseline. Finally, a second spray of distilled water only was performed following the same procedure as the humidification spray. The purpose of this spray was to rinse out the aerosol generation system and to remove any remaining airborne particulate from within the chamber.
Fig. 3. Normalized droplet diameter distribution plots with standard deviations (shaded regions around each curve) for all generated aerosols (red: no solute, orange: 10 g/L, yellow: 20 g/L, green: 30 g/L, and blue: 35 g/L). Shown distributions are stable between 5 and 10 min post generation. Mean and standard deviations were determined over the duration of this stable period for every spray. Each experimental aerosol was generated a minimum of 15 times.
Table 2. Comparison of Experimental Aerosols
Experimental aerosols were each generated a minimum of 15 times in a randomized order (e.g., Aerosol 1, Aerosol 5, Aerosol 2, Aerosol 1, etc.). Between each test, the system was opened, any standing water was drained, the walls were wiped down and dried using towels, and the internal volume was allowed to equilibriate with the laboratory environment. The temperature and RH of the laboratory environment were tracked during the experimental course, and remained approximately constant at 23°C and 40%, respectively, for the duration. The presence of potential additional airborne particulate (dust or other particulate) was controlled via filtration of building air. Background concentrations of non-experimental airborne particulates within the chamber were taken using a TSI SMPS and a Handix Scientific POPS prior to each experimental run, and found to be at least four orders of magnitude less across the 10–3000 nm cumulative diameter range than generated during experimentation.
4. RESULTS
Measurement results from the Spraytec and the transmissometer are combined to calculate the number density and droplet diameter distribution of the generated aerosols. For clarity, the droplet diameter distributions herein are given as number distributions, not volume distributions. All aerosols shown in Table 1 proved to be optically and microphysically different from one another and highly repeatable.
A. Aerosol Microphysical Properties
The aerosols generated with no solute present in the feed water had the lowest mean droplet diameter and density, showed only a single mode, and did not fully obscure the chamber across the long (1.2 m) path length.
The aerosols generated with 10 to 35 g/L salt concentration gradually increased in mean droplet diameter and mean droplet density, but decreased in maximum droplet density. In addition to a dominant diameter mode on the scale of micrometers, these aerosols showed a secondary, semi-stable diameter mode on the scale of tens of micrometers.
Droplet size distributions for all aerosols can be seen in Fig. 3. Table 2 summarizes the measured and calculated parameters for Aerosols 1–5. Standard deviations for aerosol properties are included in Table 2.
B. Processing and Trend Data
Processing was performed using our internally developed MATLAB code described in [27,30]. Segments in Fig. 3 where the normalized diameter distribution goes to zero is when total number concentration of droplets of that diameter was below the limit of detection of our instruments. The mean diameters of the first and second modes were found to vary with salt concentration present in the feed water (Fig. 4). These diameters have been fit using exponential functions, as the anticipated relationship between these parameters is exponential [see Eq. (1)]. The fit functions and their ${r^2}$ values are presented in Fig. 4.
Fig. 4. Plots of the mean diameter of the (a) primary and (b) secondary modes versus salt concentration present in the feed water. Both plots include fit lines, the equations of which can be seen near the respective lines. The peak of the secondary mode from the 10 g/L aerosol is omitted from the proposed fit as it is not a standalone peak, but rather part of a continuum. Further discussion to this point can be found in Section 5.
Finally, our application of the Beer–Lambert law and Eqs. (2) and (3) assumes that our aerosols do not enter the heavily scattering regime, defined as when incident light experiences 10 or more scattering events, wherein collimated light becomes effectively isotropic [27,32]. Our chamber is 120 cm long and would require a scattering coefficient greater than $8\;{{\rm m}^{- 1}}$ to achieve this level of multiple scattering. As this does not happen during our experimentation (see Table 2), we can assume that the Beer–Lambert law and Eqs. (2) and (3) are valid.
C. Bulk Optical Properties of Experimental Aerosols
As the microphysical properties of the aerosol change, so too do the wavelength-dependent optical properties. These bulk optical parameters are dependent on the wavelength of light used within the aerosol, the droplet diameter distribution, and the overall number density of the aerosol [10,30]. The most significant difference can be seen by comparing Aerosols 1 and 2, which were generated with no solute and 10 g/L salt, respectively. The bulk scattering coefficient of the aerosol (${\mu _s}\!$, Table 2) more than doubles when solute is added to the feed water. Subsequently, the MOR of Aerosol 2 is less than half that of Aerosol 1. By further analyzing Aerosols 3 through 5, it becomes clear that these optical properties can be finely tuned as a function of solute concentration. These results show that this system is capable of repeatably generating multiple, optically distinct aerosols.
D. Droplet Growth and Bulk Microphysical Properties of Experimental Aerosols
Our observations are in good agreement with what is predicted by Köhler theory (Section 2.A). Namely, aerosols with any salt present (${\rm C} \gt {0}\;{\rm g/L}$) show bimodal droplet diameter distributions. This is predicted by Köhler theory, which anticipates a dominant and secondary droplet diameter mode at equilibrium with other forces (deposition, collision, etc.), as discussed in Section 2.A. Changing the solute concentration in the feed water has a clear and significant impact on the microphysical properties of the aerosol. As solute concentration increases, the mean droplet diameter sizes of both modes gradually increase (see Fig. 3 and Table 2).
The aerosols also exhibit changes in the maximum and average number densities as a function of solute concentration. The increase in average number density is likely due to observed increased longevity of the aerosols. This increase in longevity can be explained by considering the solute term from Eq. (1). This term is driven by the hygroscopicity of the droplet, which is defined as the affinity of the droplet to absorb and retain water from the ambient environment [1,5]. Our data suggest that as the solute concentration increases, so too does the hygroscopicity of the resultant droplet. As a result, the generated aerosols remain in the ambient environment longer as their hygroscopicity increases. Additionally, we anticipate that droplet hygroscopicity is bounded by the (known) hygroscopicities of the solution and solute similar to chemical potential in binary mixtures [37]; however, more research will be required to investigate this further.
We believe that the decrease in the maximum number density of the generated aerosols is likely due to the fact that each aerosol was generated under the same flow rate. As such, each aerosol contained approximately the same overall liquid water content (LWC). As mean droplet diameters increased and the 10 µm mode began to form, the maximum number of droplets naturally decreased as larger droplets absorbed more of the available water.
Finally, it is worth noting that Aerosols 4 and 5 generate very similar bulk aerosols when comparing both primary mode diameter and number density. Where these aerosols significantly differ, however, is in regard to the secondary mode diameter. The secondary mode diameter of Aerosol 5 is ${\sim}4\;{\unicode{x00B5}{\rm m}}$ (or ${\sim}20\%$) larger than that of Aerosol 4. The number density of this mode in Aerosol 5 is also greater than the number densities of Aerosols 3 and 4. We believe this indicates a potential inflection point in the overall behavior of aerosols generated in this manner. At higher concentrations, we anticipate that the secondary mode will continue to increase in number density, while the primary mode will either remain relatively constant or begin to decrease in number density to compensate for the water lost to the secondary mode. These effects are likely due to the increasing dominance of the solute effect in Eq. (1) as solute concentration continues to increase. Further investigation of bulk aerosol trends at higher NaCl concentrations will need to be investigated in future work.
5. DISCUSSION AND APPLICATIONS
We repeatably generated six unique aerosols using the processes described above. As the solute concentration increased, the mean diameters of both modes of the generated aerosols did as well. As the mean droplet diameters increased, the ultimate MOR of the aerosol first decreased (the aerosol was less transmissive) but began to increase (the aerosol was more transmissive) as droplet diameters continued to grow. The following section contains a detailed discussion of our results and comparison of our experimental aerosols against real-world atmospheric aerosols and discusses potential applications for our aerosol test-bed.
A. Comparison of Generated Aerosols to Atmospheric Aerosols
We can compare the aerosols generated in our tabletop chamber to naturally occurring atmospheric aerosols by comparing droplet size distributions. Figure 5 shows the droplet size distributions of two aerosols generated in this research (Aerosols 1 and 5) and two real-world examples of fog published by Garland [2] and Gultepe [4].
Fig. 5. Comparison of two experimentally generated aerosols (Aerosols 1 and 5 from this paper) with naturally occurring fogs (Garland from [2], and Gultepe from [4]). Similar aerosols share a similar color-scheme for easy comparison, with reds indicating the Garland fog and Aerosol 1 and blues indicating the Gultepe fog and Aerosol 5.
The fog published by Garland has a tight unimodal distribution centered at just over 1 µm in diameter, very similar to Aerosol 1. This type of diameter distribution classifies these aerosols as radiation fogs [2,4]. Radiation fogs are more common inland, where there is less available humidity and hygroscopic condensation nuclei when compared to coastal regions [2,4,23]. Radiation fogs also often have a lower number density than other fogs [2,4,23], which is also in good agreement with our findings comparing Aerosols 1 and 5 in Table 2.
The fog published by Gultepe has a bimodal distribution with peaks centered around 3 and 11 µm, similar to Aerosol 5. This aerosol is often referred to as an advection fog in [4] and is formed as warm, moist air travels over a cooler surface [1,4,5]. This type of aerosol is often seen in coastal regions, where warm, humid air flows from the body of water over the cooler coast [1,4,5]. Interestingly, unlike Aerosol 5 from this work, the fog published by Gultepe has a continuous distribution between the two peaks. This behavior is similar to Aerosol 2, where the inactivated and activated modes have overlapping distributions. It is possible that this behavior is seen when the ratio of available water to condensation nuclei is relatively large. Since all aerosols in our experiments were generated with the same amount of water, it is possible that less thermodynamically preferable diameters (i.e., those not at a semi-stable point on the Köhler curve) are not able to survive long in the tabletop chamber when large droplets are present. This behavior in the Gultepe fog could also be due to real-world ambient conditions, such as wind or “pollution” of the aerosol with additional solutes beyond just salt (e.g. soot, sulfur compounds, dust, sand, etc.). Wind could cause collision or coalescence, changing the fog droplet size distribution of the fog in ways not accounted for by the Köhler equation. Varied solute types could also change the droplet size distribution by creating a mix of multiple distributions based on droplet composition. Neither of these effects was in our experiments, as the aerosols were allowed to settle before data were taken, and our work focused on only a single solute, instead of a variety. Our future work will focus on the effects of different and multiple solutes.
Being able to replicate radiative fogs is highly relevant for optical testing of systems that are intended to be deployed at inland facilities, while replicating advection fog has similar utility to testing optical systems intended for deployment in coastal regions or at sea. Finally, the ability to replicate not just one, but multiple categories of fog is relevant to optical systems that might experience both radiation and advection fogs. These optical systems could be mobile, such as those present on airplanes and in autonomous vehicles, or simply exist in regions that experience both categories of fog.
B. Application of the Tabletop Aerosol Chamber for the Transportation Industry
Nearly a quarter (25%) of all transportation accidents are caused by inclement weather, including fog, haze, and mist [3,7,8]. Experimental simulation of naturally occurring aerosols is important for testing next-generation optical equipment for transportation instrumentation, potentially making transportation safer. To function as a useful test-bed, our tabletop chamber must be able to repeatably replicate real-world aerosols to reliably generate relevant DVE metrics.
The International Civil Aviation Organization (ICAO) defines several categories of inclement visibility as standards for aviation. The categories are defined by the instrumented runway visible range (IRVR), which is used to indicate approximately what distance a pilot might be able to see when aided by an instrument landing system (ILS) [38]. This ICAO defines the most severe of these categories, Category IIIb, of less than 200 m of visibility along a runway, which is equivalent to ${\lt} {92}\;{\rm m}$ of MOR [30,38]. In Category IIIb conditions, pilots are required to navigate using instrumentation. As such, it is clearly essential that these systems be tested in relevant conditions prior to deployment. We are able to compare the aerosols generated in our tabletop chamber to real-world conditions by using [27,30]
Table 3. Experimentally Generated Aerosols, Their MOR for 532 nm Light, and ICAO Equivalent Distances
By being able to reliably replicate a variety of aviation-relevant aerosols, this chamber can test optical equipment in relevant environments prior to real-world deployment. This measurement is not only relevant to the aviation industry, but also applicable to the transportation industry at large. Furthermore, by being able to relate conditions within the tabletop aerosol chamber to real-world environments, we can begin to more quantitatively evaluate optical solutions to DVEs at large.
6. CONCLUSION
We have demonstrated the ability to tune the bulk optical properties of an aerosol via the solute concentration present in the solution used to generate the aerosol. The behavior of the generated aerosols agrees with both theory and previous studies in the literature. We have further shown that the aerosols generated in our tabletop chamber are atmospherically relevant, and analagous to real-world fogs seen in historical field studies.
In performing these experiments, we have also developed a novel, cost-effective, reconfigurable tabletop test-bed and have provided details of this test-bed and steps to generate the aerosols shown in this paper. We believe this development is important to the community as it provides a quantitatively calibrated test-bed for experimentation in five different DVEs.
7. FUTURE WORK
Future investigations will explore the effect of increasing concentrations of NaCl in the feed water as well as investigating the presented concentrations with greater resolution (i.e., 5 g/L steps in concentration instead of 10 g/L). We will also examine the impacts of different atmospherically relevant compounds, such as black carbon, on the microphysical and optical properties of aerosols generated in our tabletop chamber. As part of this study, we will also investigate the impacts of multiple compounds (e.g., salt and black carbon) on these properties as well. Finally, we will seek to understand the effect of injecting these compounds directly into an already generated aerosol, instead of incorporating them into the feed water as was done in this experiment. We hope this work will help further elucidate the complex relationship between aerosol composition and optical signals in real-world environments.
Funding
National Nuclear Security Administration (DE-NA0003525).
Acknowledgment
This research was supported by the Laboratory Directed Research and Development (LDRD) Program at Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. The authors acknowledge and thank Matthew Tezak, Steven Storch, and Laura Lemieux for their contributions to the design, assembly, and initial testing of the tabletop chambers. The authors further acknowledge and thank Dr. D. W. Bo Broadwater for internal peer review and the many helpful conversations.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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