Droplet Spectra Research Articles

Adjuvants for Drone-Based Aerial Chemical Applications to Mitigate Off-Target Drift

Off-target drift from aerial pesticide applications in croplands can be a major source of pesticide exposure to pollinators. Pesticide adjuvants (PAs) are added to pesticides but can be as toxic as pesticides’ active ingredients. Ongoing experiments have identified sodium alginate (SA) as a drift-reducing PA less toxic to honeybees. Hence, SA and fenugreek polymer (FP) have been tested as drift-reducing PAs for aerial applications using the Remotely Piloted Aerial Application System (RPAAS). Two spray experiments were carried out in the field: (i) water only (W) and (ii) water and adjuvant (WA). Droplet spectrum and on-target coverage were collected using a VisiSize P15 image analyzer and kromekote cards, respectively. The drift reduction potentials (DRPs) of the adjuvants were analyzed based on droplet size (diameters of 10%, 50%, and 90% volume) and the proportion of driftable volume with droplets < 200 µm. Compared to the W only, the W-A treatment produced larger droplets, suggesting the presence of DRP. There were 14.5%, 8.3% to 14.4%, and 2.3% to 7.7% driftable fines in the W, WA (SA), and WA (FP) treatments, respectively. The FP treatment improved the on-target coverage (3.0% to 3.1%) compared to water (2.7%). Our results indicate that SA and FP have the potential to mitigate off-target drift and protect pollinator health.

Quality of Phytosanitary Application in Ricinus communis L. Cultivation

One of the main challenges in castor bean (Ricinus communis L.) crop is controlling gray mold (A. ricini), a fungal disease. Most fungicides used are contact-based, and the plant’s architecture complicates application efficiency. The objective of this research was to evaluate droplet deposition and spectrum in different parts of the castor plant canopy using various spray tips and flow rates. The experiment was conducted at the Instituto Mato-Grossense do Algodão (IMA) in Rondonópolis, MT, using a New Holland TL 75 tractor and a Jacto Columbia Cross sprayer. The results were in percentages and a 3-factor factor analysis was performed, where A (Tip) = 2 levels, B (Volume) = 3 levels and C (Position) = 3 levels ith four replications, using Scott-Knott test and software Assistat. Treatments involved two hydraulic nozzles (JAC 80015 and JAC 8002), three flow rates (120, 150, and 180 L per ha⁻¹), and three plant positions (lower, middle, upper third). Data were analyzed using the “F” test and compared by the Scott-Knott test at a 5% significance level. The results showed that spray tips influenced VMD, NMD, and product concentration, with greater deposition and coverage observed in the upper third of the plant.

Movable surface acoustic wave tweezers: a versatile toolbox for micromanipulation

Surface acoustic wave (SAW) tweezers are a promising multifunctional micromanipulation method that controls microscale targets via patterned acoustic fields. Owing to their device structure and bonding process, most SAW tweezers have limitations in terms of controlling the position and motion of the acoustic traps, as they generate an acoustic field with a fixed region and adjust the manipulation effects via signal modulation. To address this challenge, we propose movable SAW tweezers with a multilayer structure, achieving dynamic control of their wave field and acoustic trap positions; we demonstrate their precise manipulation functions, such as translation, in-plane rotation, out-of-plane rotation, and cluster formation, on a wide spectrum of samples, including particles, bubbles, droplets, cells, and microorganisms. Our method not only improves the degree of freedom and working range of SAW tweezers but also allows for precise and selective manipulation of microtargets via microtools and localized wavefields. Owing to their flexibility, versatility, and biocompatibility, the movable SAW tweezers can be a practical platform for achieving arbitrary manipulation of microscale targets and have the potential to play significant roles in biomedical microrobotics.

Comparative analysis of transcriptome in oil biosynthesis between seeds and non-seed tissues of Symplocos paniculata fruit.

The Symplocos paniculata, a woody oil plant, has garnered attention for its oil-rich fruit, which exhibits potential for both oil production and ecological restoration endeavors, thereby presenting substantial developmental value. However, the comprehension of the distinctive oil biosynthesis and deposition strategies within the fruit's various compartments, coupled with the tissue-specific biosynthetic pathways yielding optimal fatty acid profiles, remains in its infancy. This investigation was designed to delineate the tissue specificity of oil biosynthetic disparities and to elucidate the molecular underpinnings within the fruit mesocarp and seeds of S. paniculata, employing lipidomic and transcriptomic analyses. The results revealed that oil biosynthesis within the fruit mesocarp commences approximately 40 days prior to that within the seeds, with a concomitant higher lipid content observed in the mesocarp, reaching 43% as opposed to 30% in the seeds. The fruit mesocarp was found to be enriched with palmitic acid (C16:0) and exhibited a harmonious ratio of saturated, monounsaturated, to polyunsaturated fatty acids (SFA: MUFA: PUFA=1:1:1), in stark contrast to the seed oil, which is predominantly composed of unsaturated fatty acids, accounting for 90% of its total FA content. Microstructural assessments have unveiled divergent oil deposition modalities; the fruit mesocarp oils are predominantly sequestered within oil cells (OC) and a spectrum of lipid droplets (LD), whereas the seeds predominantly harbor uniformly-sized LD. The expression patterns of pivotal genes implicated in oil biosynthesis were observed to be markedly contingent upon the tissue type and developmental stage. Notably, the light-responsive fatty acid synthase (FAS) gene demonstrated preferential transcription within the fruit mesocarp. In contrast, genes pivotal for carbon chain elongation, such as 3-ketoacyl-ACP synthase II (KASII) and fatty acyl-ACP thioesterase A (FATA), and desaturation, typified by Stearoyl-ACP desaturase (SAD) and Fatty Acid Desaturase (FAD), were noted to be more robustly transcribed within the seeds. Furthermore, isoenzyme gene families integral to the assembly of triacylglycerol (TAG), including long-chain acyl-CoA synthetases (LACSs), glycerol-3-phosphate acyltransferases (GPATs), and lysophosphatidic acid acyltransferases (LPATs), exhibited pronounced tissue specificity. This research endeavors to clarify the molecular regulatory mechanisms that oversee oil biosynthesis within both seed and non-seed tissues of oilseed-bearing plants with entire fruits. Collectively, these findings lay the groundwork and offer technical scaffolding for future targeted cultivation of woody oil plants, with the ultimate aim of augmenting fruit oil yield and refining FA compositions.

Evaluation of Autoconversion Representation in E3SMv2 Using an Ensemble of Large‐Eddy Simulations of Low‐Level Warm Clouds

AbstractIn numerical atmospheric models that treat cloud and rain droplet populations as separate condensate categories, precipitation initiation in warm clouds is often represented by an autoconversion rate , which is the rate of formation of new rain droplets through the collisions of cloud droplets. Being a function of the cloud droplet size distribution (DSD), the local is commonly parameterized as a function of DSD moments: cloud droplet number and mass concentrations. When applied in a large‐scale model, the grid‐mean must also include a correction, or enhancement factor, to account for the horizontal variability of the cloud properties across the model grid. In this study, we evaluate the Au representation in the Energy Exascale Earth System Model version 2 (E3SMv2) climate model using large‐eddy simulations (LES), which explicitly resolve cloud droplet spectra, and therefore the local , as well as its spatial variability. The analysis of an ensemble of warm low‐level cloud cases shows that the E3SMv2 formulation represents the reasonably well compared to the horizontally averaged explicitly computed rate from LES. The agreement, however, comes from a combination of an underestimated E3SM‐tuned local rate and an overestimated subgrid cloud variability enhancement factor. The latter bias is traced to neglecting the horizontal variability of and its co‐variability with in parameterizing the grid‐mean .

Design and experiment of a lightweight cloud particle imager

Abstract Accurate in-situ measurement of cloud microphysical parameters such as particle diameter, number concentration and droplet spectrum distribution are of great significance in cloud physics, climate change, numerical weather forecasting and weather modification. This work describes the design and characterization of a newly developed in situ measurement instrument for cloud and fog named lightweight cloud particle imager (LCPI). The basic measurement principle of LCPI is based on scattering light imaging of particles in a dark field. A ring LED lighting source was designed to improve the image quality. Field measurements were carried out at a high-altitude research station on Lushan Mountain. The measured size distributions showed good agreement with parallel measurements of a cloud droplet spectrometer (fog monitor-120). The field data also showed that LCPI was able to measure the size distribution of cloud droplets from 5 to 300 μm with a high spatial resolution. In addition, another observation experiment carried out in the Tuli River weather station showed that LCPI also had the ability to acquire clear images of ice crystal particles larger than 10 μm. Thus, LCPI provides an opportunity to simultaneously quantify the microphysical structure of different cloud types.

An algae-based polymer material as a pesticide adjuvant for mitigating off-target drift

Off-target pesticide drift from cropland is a major source of pesticide exposure to pollinating insects inhabiting crop and wildlands in the lower Mississippi Delta (LMD) in the USA. This study is aimed to develop a drift-reducing pesticide adjuvant that is less/nontoxic to honeybees. Ongoing toxicology experiments with two widely-used insecticides and sodium alginate (SA) pointed out reductions in honeybee mortality compared to an industry standard reference polyacrylamide (PAM). When used as an adjuvant to spray the same insecticides described above, SA did not interfere in killing the target pests. Therefore, SA has been tested as a drift-reducing pesticide adjuvant to protect honeybees. Spray experiments in the lab were carried out in four sets: (i) water only, (ii) water and adjuvant, (iii) water and pesticide, and (iv) water, pesticide and adjuvant. Each set contained 18 treatment combinations to cover the ranges in spray pressure (three), adjuvant dose (three), and spray nozzles (two). The droplet spectrum was analyzed using a P15 image analyzer. Diameters of 10 %, 50 % and 90 % volumes (DV10, DV50, and DV90), droplet velocity, standard deviation and relative span were measured. The drift reduction potential (DRP) of SA was analyzed by (i) dose, (ii) spray pressure, and (iii) nozzle type. The DRP of SA is compared to that of PAM. Additionally, three field experiments were carried out to analyze the efficiency of SA in reducing pesticide drift. The results from our experiments collectively indicate that SA has significant potential in mitigating drift as well as minimizing pesticide toxicity to honeybees.

Volume de calda e espetro de gotas no controle de Bidens pilosa e Ipomoea triloba com o herbicida Fomesafen

ABSTRACT Among weed control methods, chemical control using herbicides is one of the most widely employed due to its practicality and efficiency. However, there is still a lack of reliable information regarding the effectiveness of low-volume spraying and the droplet spectrum for contact herbicides, such as fomesafen. The objective was to determine the spray volume and droplet spectrum for applying the fomesafen herbicide and its efficacy in controlling hairy beggarticks (Bidens pilosa) and littlebell (Ipomoea triloba). The herbicide was applied using a CO2-pressurized knapsack sprayer with TT11002 spray tip spaced at 0.50 m, operating at pressures of 100 kPa (very coarse droplets) and 400 kPa (medium-sized droplets) with spray volumes of 35, 70, 140, and 280 L ha-1, obtained by varying the application speed. At the time of application, the percentage of covered area and droplet density (droplets cm-²) were evaluated on water-sensitive paper labels using the DropScope® program. The best control results for hairy beggarticks were achieved when the application was performed with very coarse droplets at a pressure of 100 kPa, with a spray volume between 65 and 280 L ha-1, droplet density exceeding 60 droplets cm-2, and coverage greater than 10%. Fomesafen does not provide effective control of littlebell.

Impact of application volume and spray droplet size on soybean harvest aid efficacy

AbstractThroughout the mid‐southern United States, indeterminate maturity group (MG) IV soybean (Glycine max L. Merr.) have largely taken the place of determinate MG V and VI soybean in order maximize yield. However, this shift towards earlier maturity groups results in more green plant material at physiological maturity. Consequently, harvest aids are of interest to producers who seek to defoliate and desiccate soybean in a timely and uniform manner. Field experiments were conducted from 2019 to 2020 to evaluate the impact of harvest aid, application volume, and spray droplet volume mean diameter (VMD) on soybean harvest aid efficacy in Mississippi. Applications of paraquat, saflufenacil, and sodium chlorate were made when soybean averaged 65% brown pods, with application volumes of 5 and 20 gal. acre−1, and VMDs of 200, 500, and 800 µm. Defoliation, desiccation, and green stems were evaluated at 3, 7, 10, and 14 days after treatment (DAT). Soybean grain yield was obtained at harvest and adjusted to 13% moisture. Harvest aid efficacy did not vary due to application volume. Spray droplet spectra with a VMD of 200 µm maximized both defoliation and desiccation efficacy. At 14 DAT, applications of paraquat and sodium chlorate resulted in 95% defoliation but were similar to saflufenacil at 89%. Applications of paraquat resulted in desiccation of 16 more percentage points than saflufenacil, and 8 more percentage points than sodium chlorate. These data indicate soybean harvest aid applications should contain paraquat. Although efficacy was maximized with spray droplet spectra with a VMD of 200 µm, the total variance in efficacy due to VMD of spray droplets was minimal.

Efecto del uso de boquillas de aire inducido y adyuvante sobre la calidad de aplicación de fitosanitarios

Spraying of phytosanitary products must ensure efficient pest control while avoiding losses that could damage human health and environment. The objective of the study was to evaluate the effect of conventional hollow cone and air-induced hydraulic nozzles, and the addition of an adjuvant (Methylated Seed Oil [MSO]) upon the application quality of a fungicide (penetration, distribution, and uniformity) and losses due to endodrift on wheat and soybean canopies. In each crop, the experimental design was completely randomized,with four replications and a factorial arrangement of nozzles x adjuvant. Droplet spectrum was evaluated by using water-sensitive cards, and brilliant blue as a tracer in the application mixture. According to the results, the MSO did not homogenize the distribution of droplets generated by air-induced nozzles in the wheat canopy, as it depended on the crop height evaluated. In addition, the MSO reduced endodrift independently of the type of nozzle used. Thedistribution of droplets on the soybean canopy depended on the height and not on the type of nozzle nor the addition of MSO. The MSO and its combination with the nozzle type did not reduce endodrift in this crop.

Reduced‐Order Modeling for Linearized Representations of Microphysical Process Rates

AbstractRepresenting cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.

Sensitivity of cloud microphysics to aerosol is highly associated with cloud water content: Implications for indirect radiative forcing

The sensitivity of cloud microphysics to aerosol loading, quantified by the aerosol-cloud interactions (ACI) index, plays a crucial role in calculating the radiative forcing due to ACI (RFaci). However, the dependence of the ACI index on liquid water content (LWC) and its impact on RFaci are often overlooked. This study aims to investigate this dependence and evaluate its implications for RFaci, based on ground in-situ aerosol-cloud observations on Mt. Lu in eastern China. The results demonstrate that the ACI index exhibits an initial increase, followed by a decline with increasing LWC. In the unconstrained LWC scenario, the ACI index, calculated based on cloud droplet number concentration (ACIn of 0.13), is found to be lower than the lower bound of ACIn (0.17 to 0.35) obtained from the constrained LWC scenario. Neglecting this LWC-dependence leads to a significant underestimation of the mean RFaci by 53%. Furthermore, when calculating RFaci using the ACI index from droplet effective diameter (ACId), implying the neglect of the dispersion effect by assuming a fixed droplet spectrum width, it results in an overestimation of RFaci by 22% compared to using ACIn. These findings shed new light on the assessment of RFaci and help reconcile differences between observed and simulated RFaci. Plain language summaryAerosol-cloud interactions are the major source of uncertainty in understanding human-induced climate change. This study focused on the relationship between aerosol and clouds, specifically investigating how the sensitivity of cloud properties to aerosol loading associates with the amount of liquid water present in the clouds. Using ground in-situ observations of clouds and aerosols on Mt. Lu in eastern China, this study showed that the sensitivity of cloud properties to aerosol loading initially increases and then decreases as the cloud water content increases. Neglecting this dependence can lead to a significant underestimation of the impact of aerosol-cloud interactions on climate. These findings provide valuable insights for improving our understanding of how aerosols and clouds interact and contribute to climate change.

Wind Tunnel Evaluation of Plant Protection Products Drift Using an Integrated Chemical–Physical Approach

The use of plant protection products (PPPs) has become fundamental to guarantee excellent field productivity. Nevertheless, their usage presents critical issues, such as the quantity of substances used, the relative toxicity, and the contamination of nearby fields caused by atmospheric drift. This study focuses on the characterization of aerosol droplets of PPPs produced by spraying a chemical marker, fluorescein, with an orchard airblast sprayer equipped with conventional hollow cone (HC) and anti-drift air inclusion (AI) nozzles, using a wind tunnel as a controlled environment. A particle/droplet image analysis was employed to study the droplet production of the nozzles, while a liquid chromatography tandem mass spectrometry (HPLC-MS/MS) analysis allowed us to evaluate samples collected using a cascade impactor located at 5 m, 10 m, and 20 m from the emission point. Overall, HC nozzles are very accurate at producing specific drop size distributions (DSDs), while AI nozzles produce a much wider DSD, concentrating the largest part of the distributed volume into droplets of a larger size. The marker concentration was much lower for the AI nozzles compared to the HC nozzles; moreover, the two nozzles show a similar trend in the coarse droplet range, while significantly differing in the fine droplet spectrum.

Ground-based cloud microphysical observations at Mount Lu in the East Asian monsoon region from 2015 to 2020

Ground-based cloud microphysical observations were continuously conducted at Mount (Mt.) Lu site from 2015 to 2020, which is located in the East Asian monsoon areas of eastern China. The statistical characteristics of cloud microphysics under different meteorological conditions were analyzed by using visibility, cloud droplet and raindrop size distribution data, and meteorological variables. The mean cloud droplet number concentration (Nc), liquid water content (LWC) and effective diameter (De) are 151 cm−3, 0.08 g m−3 and 11.4 μm, respectively. Nc and LWC observed at Mount Lu are lower than the observation results of cloud from other mountain stations all over the world. Broader cloud droplet spectra are correlated with higher temperatures and lower wind speeds. The clouds are classified into nonprecipitating, lightly-raining and raining clouds. Statistics show that lightly-raining and raining clouds account for 66% of all clouds. Nc and LWC of lightly-raining (drizzling) clouds are highest, while spectral width of raining clouds is largest. Spectral dispersion of raining clouds converges to the narrow range of 0.4–0.7. Notably, medium sized cloud droplets (11–40 μm) promote the formation of large cloud droplets (40–50 μm, pre-drizzle size) and drizzle drops which initiate the auto-conversion from condensation to collision-coalescence growth. This study relying on in-situ continuous cloud observation from mountain station are helpful to understand cloud microphysical structures in East Asian monsoon region, and fitting parameters also provide observational evidence for numerical models.

Development and evaluation of a rope climbing sprayer robot for greenhouse environment

The ever-increasing population has promoted inventions and innovations such as greenhouse cultivation to enhance agricultural production in terms of both quantity and quality. Despite its advantages, greenhouse cultivation provides favorable conditions for the spread of diseases and pests. While agricultural pesticides have been exploited to protect plants and crops and to combat their diseases, they are unfortunately a major source of environmental pollution. In this study, a smart sprayer rope climbing robot was designed to spray the plant row from the side in two directions (top to bottom and vice versa). Using the Mylar sheets to collect and measure droplets, the robot's performance was evaluated in terms of the effects of nozzle speed (12, 15, and 18 m/min), pressure (1.4, 2.6, and 3.6 bar), and type (flat-fan and hollow-cone nozzles) as well as leaf position (upper, middle, and lower) on such indices as numerical median diameter (NMD), volumetric median diameter (VMD), coverage percentage, uniformity index of droplet spectrum, and droplet number per unit area. In addition, comparisons were made between the proposed sprayer robot and the two back-pack and trolley sprayers in terms of their pesticide consumption and user exposure levels. Maximum and minimum pesticide consumption rates of the robot were recorded at 1.16 L and 2.6 L, respectively, when it was used to spray two rows 15 m long in a greenhouse. Finally, pesticide consumption rate by the robot was found to be by 55% and 82.9% less than those recorded for the backpack and trolley sprayers, respectively.

Variation of glyphosate spray volume in the drying of ground cover plants with an unmanned aerial vehicle

The presence of straw on the soil is crucial for enhancing its physical, chemical, and biological conditions in no-tillage cropping systems, while contributing to weed control. This study aimed to determine the optimal spray volume for desiccating ground cover plants using glyphosate applied by an unmanned aerial vehicle (UAV). Treatments included an untreated control and glyphosate spraying at 5, 10, 15, 20, and 25 L ha−1 spray solution. The study was conducted in three fields in the State of Paraná, Brazil. Spraying variables were assessed using water-sensitive paper and the e-Sprinkle software, and included, covered area (%), recovered spray volume (L ha−1), droplet impact density, coefficient of variation for droplet diameter (CV%), and relative amplitude (SPAN). Herbicidal efficacy and dry biomass of cover plants were also evaluated. Increasing spray volume improved covered area, recovered volume, droplet impact density, and droplet diameter-spectrum variation but had no effect on droplet spectrum homogeneity, control of black oat (Avena strigosa), turnip (Raphanus sativus), or buckwheat (Fagopyrum esculentum). Spray volumes ranging from 5.0 to 25 L ha−1, applied with a UAV, provided satisfactory desiccation percentages of ground cover plants irrespective of species or location. Thus, for desiccating ground cover plants with glyphosate applied by UAV, a volume as low as 5.0 L ha−1 is effective.

Optimization of the Air Cleaning Properties of Fog

Fog droplets are very often used as a cleaning agent when air pollution can be dangerous for health conditions and ecosystem. This work presents a new system to optimize the cleaning properties of fog by tuning its microphysical parameters. For this purpose, a newly developed system, which is based on the electromagnetic echo effect (EMEE) sensor, is used to detect the most efficient interaction between fog and impurities, i.e., which fog droplets can be used to most effectively clean a certain type of pollutant from the air. Fog droplet spectra controlled by the nozzle pressure system can be used to effectively remove pollutants from the air. For this purpose, an automated system for aerosol generation can allow an accurate control over the fog microphysical parameters and the use of fluids with specific concentrations of pulverized chemical compounds. Fog droplet size distribution is controlled by the feeding gas pressure at the nozzle and chemical simulants. The experimental results showed that the microphysical parameters (MP) are directly related to the impurity of species used in the cleanup simulation process. The MP parameters of fog are liquid water content (LWC), droplet mean radius (Rm), droplet number concentration (Nd), and both aerosol type and mass concentration. In the lab testing, harmless simulants of CBRN (chemical, biological, radiological and nuclear) species were used. During the tests, fog droplet size distribution is controlled by the air pressure at the nozzle and simulants. It is concluded that an integrated fog generator system (IFGS) with EMEE sensor developed in the current work can be utilized broadly to control fog microphysical parameters, leading to an optimum aerosol/chemical species’ cleaning process.

Control of Urochloa decumbens Using Glyphosate Applied by Remotely Piloted Aircraft and Ground Sprayer with Different Spray Nozzles.

The use of remotely piloted aircraft (RPA) to spray pesticides currently occurs, but knowledge about this technology is lacking due to the different locations, targets, and products applied. The objective of this study was to evaluate the control of Urochloa decumbens with glyphosate applied using an RPA (10 L ha-1) equipped with different spray nozzles (XR 11001 and AirMix 11001). For the purpose of comparison, ground application was also performed (100 L ha-1). The deposition was evaluated by means of the quantification of a tracer by spectrophotometry, the droplet spectrum was evaluated with water-sensitive paper, and the control efficiency was evaluated based on visual measurements with percentage scores. Statistical process control was used to analyse the quality of the deposition in the area. The results showed that the application via RPA presented a greater amount of tracer on the leaves than the ground application, suggesting that the former is a good option for application, even providing a lower coverage and number of droplets per area. Both application methods were effective at controlling Urochloa decumbens. The nozzles showed potential for use in applications, with control efficiency higher than 84% from 21 days after application. The percentage of droplets smaller than 100 μm in the applications was less than 5%. No nonrandom behaviour was observed during deposition, indicating a high-quality process.

Development of a multi-stage fog droplet screening system based on the virtual impact principle.

Accurately measuring fog droplet spectra is essential for understanding fog's formation, dissipation, and composition, which makes a challenge to the performance of droplet sampling and measurement systems. Standard particles such as glass beads are widely used to characterize their performance. However, the disparities between glass beads and fog droplets, including refractivity, size distribution, and composition, may lead to calibration errors. In this context, we developed a three-stage fog droplet screening system based on the virtual impact principle. We determined the Stokes number and the diameter of the acceleration nozzle through theoretical analysis. Subsequently, we utilized the computational fluid dynamics software Fluent to explore the influence of key system parameters on screening efficiency, including the diameter of the collection nozzle (D1) and the distance between the acceleration nozzle and the collection nozzle (S). The simulation results indicated that the screening efficiency improved with S. The best performance was achieved when D1 = 1.35 D0 and S = 1.90 D0 (where D0 represents the diameter of the acceleration nozzle), resulting in an average screening efficiency of 75.4%. Finally, we conducted experiments to validate the effectiveness of the screening system. The screening efficiency of each outlet was estimated at 42.2%, 66.1%, 84.0%, and 95.3%, with differences of 2.0%, 3.3%, 4.1%, and 4.7% compared to the simulations. The average screening efficiency was 71.9%, with a deviation of 3.5% from the simulation. These findings demonstrated that the screening system could provide an alternative technical apparatus for characterizing droplet sampling and measurement systems.

Particle size distribution measurements under supercooled large drop conditions with Cloud Combination Probe in CARDC icing wind tunnel

Particle size distribution (PSD) of the icing cloud is one of the most important icing parameters for icing wind tunnels. However, accurate measurements of the PSD are challenging for supercooled large drop (SLD) conditions due to the wide droplet size spectrum and low number density of large droplets. In this study, PSD measurements with the Cloud Combination Probe (CCP) are comprehensively investigated in the CARDC icing wind tunnel. A method based on the CCP is developed for composite PSD calculation, and an icing cloud measurement test with 79 test conditions covering Appendix C and freezing drizzle conditions, is conducted for accuracy assessment of the CCP and reliability validation of the PSD calculation method. Based on the test, the effects of spurious particle images are examined first, followed by a quantitative assessment of the influences of particle size calculation and sample volume calculation on the PSD, medium volume diameter (MVD), and liquid water content (LWC). The results show that all PSD measurements are adversely impacted by spurious particle images, primarily caused by coincidence errors. For five typical particle size parameters, the effects of particle size calculation on the PSD are significant, with maximum relative deviations of about 110% for MVD and 63% for LWC. The influences of Depth-of-Field factors are not noticeable except for the manufacturer factor. Additionally, the dead time also dramatically impacts the PSD, resulting in a decrease in the MVD and LWC with maximum decreases of about 60% and 43%, respectively. Finally, the LWCs determined by composite PSDs show good agreement with the reference LWCs obtained by the SEA multi-element sensor, where the relative deviation is within ±10% for the majority of measured points. This demonstrates the CCP is suitable for wind tunnel measurements of PSDs under Appendix C and freezing drizzle conditions employing the PSD calculation method. To accurately calculate the PSD, spurious particle image removal and dead-time correction should be considered, and out-of-focus correction diameter and in-house factor are preferred.